KaQyn/huggingface_stack · Datasets at Hugging Face

# Lo que 🤗 Transformers puede hacer 🤗 Transformers es una biblioteca de modelos preentrenados de última generación para procesamiento del lenguaje natural (NLP, por sus siglas en inglés), visión por computadora y tareas de procesamiento de audio y voz. No solo contiene modelos Transformer, sino también modelos no Transformer como redes convolucionales modernas para tareas de visión por computadora. Si observas algunos de los productos de consumo más populares hoy en día, como teléfonos inteligentes, aplicaciones y televisores, es probable que haya alguna tecnología de aprendizaje profundo detrás. ¿Quieres quitar un objeto de fondo de una foto tomada por tu teléfono inteligente? Este es un ejemplo de una tarea de segmentación panóptica (no te preocupes si aún no sabes qué significa, ¡lo describiremos en las siguientes secciones!). Esta página proporciona una descripción general de las diferentes tareas de procesamiento de audio y voz, visión por computadora y NLP que se pueden resolver con la biblioteca 🤗 Transformers en solo tres líneas de código. ## Audio Las tareas de procesamiento de audio y voz son un poco diferentes de las otras modalidades principalmente porque el audio como entrada es una señal continua. A diferencia del texto, una forma de onda de audio cruda no se puede dividir ordenadamente en fragmentos discretos de la misma manera en que una oración puede dividirse en palabras. Para superar esto, la señal de audio cruda generalmente se muestrea a intervalos regulares. Si tomas más muestras dentro de un intervalo, la tasa de muestreo es mayor y el audio se asemeja más a la fuente de audio original. Enfoques anteriores preprocesaban el audio para extraer características útiles. Ahora es más común comenzar las tareas de procesamiento de audio y voz alimentando directamente la forma de onda de audio cruda a un codificador de características para extraer una representación de audio. Esto simplifica el paso de preprocesamiento y permite que el modelo aprenda las características más esenciales. ### Clasificación de audio La clasificación de audio es una tarea que etiqueta datos de audio con un conjunto predefinido de clases. Es una categoría amplia con muchas aplicaciones específicas, algunas de las cuales incluyen: * clasificación de escena acústica: etiquetar audio con una etiqueta de escena ("oficina", "playa", "estadio") * detección de eventos acústicos: etiquetar audio con una etiqueta de evento de sonido ("bocina de automóvil", "llamada de ballena", "cristal rompiéndose") * etiquetado: etiquetar audio que contiene varios sonidos (canto de pájaros, identificación de altavoces en una reunión) * clasificación de música: etiquetar música con una etiqueta de género ("metal", "hip-hop", "country") ```py >>> from transformers import pipeline >>> classifier = pipeline(task="audio-classification", model="superb/hubert-base-superb-er") >>> preds = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] >>> preds [{'score': 0.4532, 'label': 'hap'}, {'score': 0.3622, 'label': 'sad'}, {'score': 0.0943, 'label': 'neu'}, {'score': 0.0903, 'label': 'ang'}] ``` ### Reconocimiento automático del habla El reconocimiento automático del habla (ASR, por sus siglas en inglés) transcribe el habla a texto. Es una de las tareas de audio más comunes, en parte debido a que el habla es una forma natural de comunicación humana. Hoy en día, los sistemas ASR están integrados en productos de tecnología "inteligente" como altavoces, teléfonos y automóviles. Podemos pedirle a nuestros asistentes virtuales que reproduzcan música, establezcan recordatorios y nos informen sobre el clima. Pero uno de los desafíos clave que las arquitecturas Transformer han ayudado a superar es en los idiomas con recursos limitados. Al preentrenar con grandes cantidades de datos de habla, afinar el modelo solo con una hora de datos de habla etiquetados en un idioma con recursos limitados aún puede producir resultados de alta calidad en comparación con los sistemas ASR anteriores entrenados con 100 veces más datos etiquetados. ```py >>> from transformers import pipeline >>> transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-small") >>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac") {'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'} ``` ## Visión por computadora Una de las primeras y exitosas tareas de visión por computadora fue reconocer imágenes de números de código postal utilizando una [red neuronal convolucional](glossary#convolution) (CNN, por sus siglas en inglés). Una imagen está compuesta por píxeles, y cada píxel tiene un valor numérico. Esto facilita representar una imagen como una matriz de valores de píxeles. Cada combinación particular de valores de píxeles describe los colores de una imagen. Dos formas generales en las que se pueden resolver las tareas de visión por computadora son: 1. Utilizar convoluciones para aprender las características jerárquicas de una imagen, desde características de bajo nivel hasta cosas abstractas de alto nivel. 2. Dividir una imagen en parches y utilizar un Transformer para aprender gradualmente cómo cada parche de imagen se relaciona entre sí para formar una imagen. A diferencia del enfoque ascendente preferido por una CNN, esto es como comenzar con una imagen borrosa y luego enfocarla gradualmente. ### Clasificación de imágenes La clasificación de imágenes etiqueta una imagen completa con un conjunto predefinido de clases. Como la mayoría de las tareas de clasificación, hay muchos casos prácticos para la clasificación de imágenes, algunos de los cuales incluyen: * salud: etiquetar imágenes médicas para detectar enfermedades o monitorear la salud del paciente * medio ambiente: etiquetar imágenes de satélite para monitorear la deforestación, informar la gestión de áreas silvestres o detectar incendios forestales * agricultura: etiquetar imágenes de cultivos para monitorear la salud de las plantas o imágenes de satélite para el monitoreo del uso del suelo * ecología: etiquetar imágenes de especies animales o vegetales para monitorear poblaciones de vida silvestre o rastrear especies en peligro de extinción ```py >>> from transformers import pipeline >>> classifier = pipeline(task="image-classification") >>> preds = classifier( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ... ) >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] >>> print(*preds, sep="\n") {'score': 0.4335, 'label': 'lynx, catamount'} {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'} {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'} {'score': 0.0239, 'label': 'Egyptian cat'} {'score': 0.0229, 'label': 'tiger cat'} ``` ### Detección de objetos A diferencia de la clasificación de imágenes, la detección de objetos identifica múltiples objetos dentro de una imagen y las posiciones de los objetos en la imagen (definidas por el cuadro delimitador). Algunas aplicaciones ejemplares de la detección de objetos incluyen: * vehículos autónomos: detectar objetos de tráfico cotidianos como otros vehículos, peatones y semáforos * teledetección: monitoreo de desastres, planificación urbana y pronóstico del tiempo * detección de defectos: detectar grietas o daños estructurales en edificios y defectos de fabricación ```py >>> from transformers import pipeline >>> detector = pipeline(task="object-detection") >>> preds = detector( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ... ) >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"], "box": pred["box"]} for pred in preds] >>> preds [{'score': 0.9865, 'label': 'cat', 'box': {'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}] ``` ### Segmentación de imágenes La segmentación de imágenes es una tarea a nivel de píxeles que asigna cada píxel en una imagen a una clase. A diferencia de la detección de objetos, que utiliza cuadros delimitadores para etiquetar y predecir objetos en una imagen, la segmentación es más granular. La segmentación puede detectar objetos a nivel de píxeles. Hay varios tipos de segmentación de imágenes: * segmentación de instancias: además de etiquetar la clase de un objeto, también etiqueta cada instancia distinta de un objeto ("perro-1", "perro-2") * segmentación panóptica: una combinación de segmentación semántica y de instancias; etiqueta cada píxel con una clase semántica **y** cada instancia distinta de un objeto Las tareas de segmentación son útiles en vehículos autónomos para crear un mapa a nivel de píxeles del mundo que los rodea para que puedan navegar de manera segura alrededor de peatones y otros vehículos. También es útil en imágenes médicas, donde la mayor granularidad de la tarea puede ayudar a identificar células anormales o características de órganos. La segmentación de imágenes también se puede utilizar en comercio electrónico para probar virtualmente la ropa o crear experiencias de realidad aumentada superponiendo objetos en el mundo real a través de tu cámara. ```py >>> from transformers import pipeline >>> segmenter = pipeline(task="image-segmentation") >>> preds = segmenter( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ... ) >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] >>> print(*preds, sep="\n") {'score': 0.9879, 'label': 'LABEL_184'} {'score': 0.9973, 'label': 'snow'} {'score': 0.9972, 'label': 'cat'} ``` ### Estimación de profundidad La estimación de profundidad predice la distancia de cada píxel en una imagen desde la cámara. Esta tarea de visión por computadora es especialmente importante para la comprensión y reconstrucción de escenas. Por ejemplo, en los vehículos autónomos, es necesario entender qué tan lejos están los objetos como peatones, señales de tráfico y otros vehículos para evitar obstáculos y colisiones. La información de profundidad también es útil para construir representaciones 3D a partir de imágenes 2D y se puede utilizar para crear representaciones 3D de alta calidad de estructuras biológicas o edificios. Hay dos enfoques para la estimación de profundidad: * estéreo: las profundidades se estiman comparando dos imágenes de la misma escena desde ángulos ligeramente diferentes * monocular: las profundidades se estiman a partir de una sola imagen ```py >>> from transformers import pipeline >>> depth_estimator = pipeline(task="depth-estimation") >>> preds = depth_estimator( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" ... ) ``` ## Procesamiento del lenguaje natural Las tareas de procesamiento del lenguaje natural (NLP, por sus siglas en inglés) están entre los tipos de tareas más comunes porque el texto es una forma natural de comunicación para nosotros. Para convertir el texto en un formato reconocido por un modelo, es necesario tokenizarlo. Esto significa dividir una secuencia de texto en palabras o subpalabras separadas (tokens) y luego convertir estos tokens en números. Como resultado, puedes representar una secuencia de texto como una secuencia de números, y una vez que tienes una secuencia de números, se puede ingresar a un modelo para resolver todo tipo de tareas de NLP. ### Clasificación de texto Al igual que las tareas de clasificación en cualquier modalidad, la clasificación de texto etiqueta una secuencia de texto (puede ser a nivel de oración, párrafo o documento) de un conjunto predefinido de clases. Hay muchas aplicaciones prácticas para la clasificación de texto, algunas de las cuales incluyen: * análisis de sentimientos: etiquetar texto según alguna polaridad como `positivo` o `negativo`, lo que puede informar y respaldar la toma de decisiones en campos como política, finanzas y marketing * clasificación de contenido: etiquetar texto según algún tema para ayudar a organizar y filtrar información en noticias y feeds de redes sociales (`clima`, `deportes`, `finanzas`, etc.) ```py >>> from transformers import pipeline >>> classifier = pipeline(task="sentiment-analysis") >>> preds = classifier("Hugging Face is the best thing since sliced bread!") >>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds] >>> preds [{'score': 0.9991, 'label': 'POSITIVE'}] ``` ### Clasificación de tokens En cualquier tarea de NLP, el texto se procesa separando la secuencia de texto en palabras o subpalabras individuales. Estas se conocen como [tokens](glossary#token). La clasificación de tokens asigna a cada token una etiqueta de un conjunto predefinido de clases. Dos tipos comunes de clasificación de tokens son: * reconocimiento de entidades nombradas (NER, por sus siglas en inglés): etiquetar un token según una categoría de entidad como organización, persona, ubicación o fecha. NER es especialmente popular en entornos biomédicos, donde puede etiquetar genes, proteínas y nombres de medicamentos * etiquetado de partes del discurso (POS, por sus siglas en inglés): etiquetar un token según su parte del discurso, como sustantivo, verbo o adjetivo. POS es útil para ayudar a los sistemas de traducción a comprender cómo dos palabras idénticas son gramaticalmente diferentes (por ejemplo, "corte" como sustantivo versus "corte" como verbo) ```py >>> from transformers import pipeline >>> classifier = pipeline(task="ner") >>> preds = classifier("Hugging Face is a French company based in New York City.") >>> preds = [ ... { ... "entity": pred["entity"], ... "score": round(pred["score"], 4), ... "index": pred["index"], ... "word": pred["word"], ... "start": pred["start"], ... "end": pred["end"], ... } ... for pred in preds ... ] >>> print(*preds, sep="\n") {'entity': 'I-ORG', 'score': 0.9968, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2} {'entity': 'I-ORG', 'score': 0.9293, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7} {'entity': 'I-ORG', 'score': 0.9763, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12} {'entity': 'I-MISC', 'score': 0.9983, 'index': 6, 'word': 'French', 'start': 18, 'end': 24} {'entity': 'I-LOC', 'score': 0.999, 'index': 10, 'word': 'New', 'start': 42, 'end': 45} {'entity': 'I-LOC', 'score': 0.9987, 'index': 11, 'word': 'York', 'start': 46, 'end': 50} {'entity': 'I-LOC', 'score': 0.9992, 'index': 12, 'word': 'City', 'start': 51, 'end': 55} ``` ### Respuestas a preguntas Responder preguntas es otra tarea a nivel de tokens que devuelve una respuesta a una pregunta, a veces con contexto (dominio abierto) y otras veces sin contexto (dominio cerrado). Esta tarea ocurre cuando le preguntamos algo a un asistente virtual, como si un restaurante está abierto. También puede proporcionar soporte al cliente o técnico y ayudar a los motores de búsqueda a recuperar la información relevante que estás buscando. Hay dos tipos comunes de respuestas a preguntas: * extractivas: dada una pregunta y algún contexto, la respuesta es un fragmento de texto del contexto que el modelo debe extraer * abstractivas: dada una pregunta y algún contexto, la respuesta se genera a partir del contexto; este enfoque lo maneja la [`Text2TextGenerationPipeline`] en lugar del [`QuestionAnsweringPipeline`] que se muestra a continuación ```py >>> from transformers import pipeline >>> question_answerer = pipeline(task="question-answering") >>> preds = question_answerer( ... question="What is the name of the repository?", ... context="The name of the repository is huggingface/transformers", ... ) >>> print( ... f"score: {round(preds['score'], 4)}, start: {preds['start']}, end: {preds['end']}, answer: {preds['answer']}" ... ) score: 0.9327, start: 30, end: 54, answer: huggingface/transformers ``` ### Resumir Al resumir se crea una versión más corta de un texto más largo mientras intenta preservar la mayor parte del significado del documento original. Resumir es una tarea de secuencia a secuencia; produce una secuencia de texto más corta que la entrada. Hay muchos documentos de formato largo que se pueden resumir para ayudar a los lectores a comprender rápidamente los puntos principales. Proyectos de ley legislativos, documentos legales y financieros, patentes y artículos científicos son algunos ejemplos de documentos que podrían resumirse para ahorrar tiempo a los lectores y servir como ayuda para la lectura. Al igual que en las respuestas a preguntas, hay dos tipos de resumen: * extractiva: identifica y extrae las oraciones más importantes del texto original * abstractiva: genera el resumen objetivo (que puede incluir nuevas palabras no presentes en el documento de entrada) a partir del texto original; el [`SummarizationPipeline`] utiliza el enfoque abstractivo ```py >>> from transformers import pipeline >>> summarizer = pipeline(task="summarization") >>> summarizer( ... "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles." ... ) [{'summary_text': ' The Transformer is the first sequence transduction model based entirely on attention . It replaces the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention . For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers .'}] ``` ### Traducción La traducción convierte una secuencia de texto en un idioma a otro. Es importante para ayudar a personas de diferentes orígenes a comunicarse entre sí, traducir contenido para llegar a audiencias más amplias e incluso ser una herramienta de aprendizaje para ayudar a las personas a aprender un nuevo idioma. Al igual que resumir, la traducción es una tarea de secuencia a secuencia, lo que significa que el modelo recibe una secuencia de entrada y devuelve una secuencia de salida objetivo. En sus primeros días, los modelos de traducción eran principalmente monolingües, pero recientemente ha habido un creciente interés en modelos multilingües que pueden traducir entre muchas combinaciones de idiomas. ```py >>> from transformers import pipeline >>> text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning." >>> translator = pipeline(task="translation", model="t5-small") >>> translator(text) [{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}] ``` ### Modelado de lenguaje El modelado de lenguaje es una tarea que predice una palabra en una secuencia de texto. Se ha vuelto una tarea de NLP muy popular porque un modelo de lenguaje preentrenado puede ser afinado para muchas otras tareas secundarias. Últimamente, ha habido mucho interés en modelos de lenguaje grandes (LLM, por sus siglas en inglés) que demuestran aprendizaje de cero o con pocas muestras (zero- or few-shot learning). ¡Esto significa que el modelo puede resolver tareas para las cuales no fue entrenado explícitamente! Los modelos de lenguaje se pueden utilizar para generar texto fluido y convincente, aunque debes tener cuidado, ya que el texto no siempre puede ser preciso. Hay dos tipos de modelado de lenguaje: * causal: el objetivo del modelo es predecir el próximo token en una secuencia, y los tokens futuros están enmascarados ```py >>> from transformers import pipeline >>> prompt = "Hugging Face is a community-based open-source platform for machine learning." >>> generator = pipeline(task="text-generation") >>> generator(prompt) # doctest: +SKIP ``` * enmascarado: el objetivo del modelo es predecir un token enmascarado en una secuencia con acceso completo a los tokens en la secuencia ```py >>> text = "Hugging Face is a community-based open-source <mask> for machine learning." >>> fill_mask = pipeline(task="fill-mask") >>> preds = fill_mask(text, top_k=1) >>> preds = [ ... { ... "score": round(pred["score"], 4), ... "token": pred["token"], ... "token_str": pred["token_str"], ... "sequence": pred["sequence"], ... } ... for pred in preds ... ] >>> preds [{'score': 0.2236, 'token': 1761, 'token_str': ' platform', 'sequence': 'Hugging Face is a community-based open-source platform for machine learning.'}] ``` ## Multimodal Las tareas multimodales requieren que un modelo procese múltiples modalidades de datos (texto, imagen, audio, video) para resolver un problema particular. La descripción de imágenes es un ejemplo de una tarea multimodal en la que el modelo toma una imagen como entrada y produce una secuencia de texto que describe la imagen o algunas propiedades de la imagen. Aunque los modelos multimodales trabajan con diferentes tipos de datos o modalidades, internamente, los pasos de preprocesamiento ayudan al modelo a convertir todos los tipos de datos en embeddings (vectores o listas de números que contienen información significativa sobre los datos). Para una tarea como la descripción de imágenes, el modelo aprende las relaciones entre los embeddings de imágenes y los embeddings de texto. ### Respuestas a preguntas de documentos Las respuestas a preguntas de documentos es una tarea que responde preguntas en lenguaje natural a partir de un documento. A diferencia de una tarea de respuestas a preguntas a nivel de token que toma texto como entrada, las respuestas a preguntas de documentos toman una imagen de un documento como entrada junto con una pregunta sobre el documento y devuelven una respuesta. Las respuestas a preguntas de documentos pueden usarse para analizar documentos estructurados y extraer información clave de ellos. En el ejemplo a continuación, el monto total y el cambio debido se pueden extraer de un recibo. ```py >>> from transformers import pipeline >>> from PIL import Image >>> import requests >>> url = "https://datasets-server.huggingface.co/assets/hf-internal-testing/example-documents/--/hf-internal-testing--example-documents/test/2/image/image.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> doc_question_answerer = pipeline("document-question-answering", model="magorshunov/layoutlm-invoices") >>> preds = doc_question_answerer( ... question="What is the total amount?", ... image=image, ... ) >>> preds [{'score': 0.8531, 'answer': '17,000', 'start': 4, 'end': 4}] ``` Con suerte, esta página te ha proporcionado más información de fondo sobre todos los tipos de tareas en cada modalidad y la importancia práctica de cada una. En la próxima [sección](tasks_explained), aprenderás **cómo** 🤗 Transformers trabaja para resolver estas tareas.

transformers/docs/source/es/task_summary.md/0

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# Debugging ## Debug dei problemi di rete multi-GPU Quando addestri o fai inferenza con `DistributedDataParallel` e GPU multiple, se si verificano problemi di intercomunicazione tra processi e/o nodi, puoi utilizzare il seguente script per diagnosticare i problemi della rete. ```bash wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py ``` Per esempio per testare come 2 GPU interagiscono fai: ```bash python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py ``` Se entrambi i processi sono in grado di comunicare tra loro e di allocare la memoria della GPU, ciascuno di essi stamperà lo stato OK. Per più GPU o nodi adatta gli argumenti nello script. All'interno dello script di diagnostica troverai molti altri dettagli e anche una guida per eseguirlo in ambiente SLURM. Un livello di debug superiore è aggiungere la variabile d'ambiente `NCCL_DEBUG=INFO` come di seguito: ```bash NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py ``` In questo modo si scaricano molte informazioni di debug relative a NCCL, che puoi cercare online in caso di problemi. Oppure, se non hai la sicurezza di come interpretare l'output, puoi condividere il file di log in una Issue. ## Rilevamento di Underflow e Overflow <Tip> Questa funzionalità al momento è disponibile solo per PyTorch. </Tip> <Tip> Per addestramento multi-GPU richiede DDP (`torch.distributed.launch`). </Tip> <Tip> Questa funzionalità può essere usata con modelli basati su `nn.Module`. </Tip> Se inizi a ottenere `loss=NaN` o il modello presenta qualche altro comportamento anomalo a causa di valori `inf` o `nan` in attivazioni o nei pesi, è necessario scoprire dove si verifica il primo underflow o overflow e cosa lo ha determinato. Fortunatamente è possibile farlo facilmente attivando un modulo speciale che effettuerà il rilevamento automaticamente. Se stai usando [`Trainer`], hai bisogno di aggiungere solo: ```bash --debug underflow_overflow ``` ai normali argomenti della riga di comando, o passa `debug="underflow_overflow"` quando viene creato l'oggetto [`TrainingArguments`]. Se stai usando il tuo ciclo di allenamento o un altro trainer, puoi ottenere lo stesso risultato con: ```python from .debug_utils import DebugUnderflowOverflow debug_overflow = DebugUnderflowOverflow(model) ``` [`~debug_utils.DebugUnderflowOverflow`] inserisce dei ganci nel modello che dopo ogni chiamata testeranno le variabili di ingresso e di uscita e anche i pesi del modulo corrispondente. Non appena viene rilevato `inf` o o `nan` in almeno un elemento delle attivazioni o dei pesi, il programma lo notifica e stampa un rapporto come il seguente (questo è stato rilevato con `google/mt5-small` sotto fp16 mixed precision): ``` Detected inf/nan during batch_number=0 Last 21 forward frames: abs min abs max metadata encoder.block.1.layer.1.DenseReluDense.dropout Dropout 0.00e+00 2.57e+02 input[0] 0.00e+00 2.85e+02 output [...] encoder.block.2.layer.0 T5LayerSelfAttention 6.78e-04 3.15e+03 input[0] 2.65e-04 3.42e+03 output[0] None output[1] 2.25e-01 1.00e+04 output[2] encoder.block.2.layer.1.layer_norm T5LayerNorm 8.69e-02 4.18e-01 weight 2.65e-04 3.42e+03 input[0] 1.79e-06 4.65e+00 output encoder.block.2.layer.1.DenseReluDense.wi_0 Linear 2.17e-07 4.50e+00 weight 1.79e-06 4.65e+00 input[0] 2.68e-06 3.70e+01 output encoder.block.2.layer.1.DenseReluDense.wi_1 Linear 8.08e-07 2.66e+01 weight 1.79e-06 4.65e+00 input[0] 1.27e-04 2.37e+02 output encoder.block.2.layer.1.DenseReluDense.dropout Dropout 0.00e+00 8.76e+03 input[0] 0.00e+00 9.74e+03 output encoder.block.2.layer.1.DenseReluDense.wo Linear 1.01e-06 6.44e+00 weight 0.00e+00 9.74e+03 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense 1.79e-06 4.65e+00 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.dropout Dropout 3.18e-04 6.27e+04 input[0] 0.00e+00 inf output ``` L'output di esempio è stato tagliato al centro per brevità. La seconda colonna mostra il valore dell'elemento più grande in assoluto,così se osserviamo da vicino gli ultimi istanti, input e output sono nel range di `1e4`. Questo addestramento è stato eseguito con una mixed precision fp16 e l'ultimo passo usciva fuori (sotto `fp16` il valore più grande prima di `inf` è `64e3`). Per evitare overflows sotto `fp16` le attivazionioni devono rimanere molto al di sotto di `1e4`, perché `1e4 * 1e4 = 1e8` quindi qualsiasi moltiplicazione di matrice con grandi attivazioni porterà a una condizione di overflow numerico. All'inizio della traccia è possibile scoprire a quale lotto si è verificato il problema (questo `Detected inf/nan during batch_number=0` significa che il problema si è verificato nel primo lotto). Ogni frame segnalato inizia dichiarando la voce completamente qualificata per il modulo corrispondente per il quale il frame è stato segnalato. Se osserviamo il seguente frame: ``` encoder.block.2.layer.1.layer_norm T5LayerNorm 8.69e-02 4.18e-01 weight 2.65e-04 3.42e+03 input[0] 1.79e-06 4.65e+00 output ``` Questo, `encoder.block.2.layer.1.layer_norm` indica che si tratta di un layer norm nel primo layer, del secondo blocco dell'encoder. E le chiamata specifica di `forward` è `T5LayerNorm`. Osserviamo gli ultimi frame del report: ``` Detected inf/nan during batch_number=0 Last 21 forward frames: abs min abs max metadata [...] encoder.block.2.layer.1.DenseReluDense.wi_0 Linear 2.17e-07 4.50e+00 weight 1.79e-06 4.65e+00 input[0] 2.68e-06 3.70e+01 output encoder.block.2.layer.1.DenseReluDense.wi_1 Linear 8.08e-07 2.66e+01 weight 1.79e-06 4.65e+00 input[0] 1.27e-04 2.37e+02 output encoder.block.2.layer.1.DenseReluDense.wo Linear 1.01e-06 6.44e+00 weight 0.00e+00 9.74e+03 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.DenseReluDense T5DenseGatedGeluDense 1.79e-06 4.65e+00 input[0] 3.18e-04 6.27e+04 output encoder.block.2.layer.1.dropout Dropout 3.18e-04 6.27e+04 input[0] 0.00e+00 inf output ``` L'ultimo frame report per la funzione `Dropout.forward` con la prima voce per l'unico input e la seconda per l'unico output. Si può notare che è stato richiamato da un attibuto `dropout` dentro la classe `DenseReluDense`. Si può notare che ciò è avvenuto durante il primo strato, del 2° blocco, durante il primissimo lotto. Infine, gli elementi di input più grandi in assoluto sono stati `6.27e+04` e l'equivalente per l'output era `inf`. Puoi vedere qui, che `T5DenseGatedGeluDense.forward` risulta in output activations, il cui valore massimo assoluto era circa 62,7K, che è molto vicino al limite massimo di 64K di fp16. Nel prossimo frame abbiamo `Dropout` che rinormalizza i pesi, dopo aver azzerato alcuni elementi, il che spinge il valore massimo assoluto a più di 64K e si verifica un overflow.(`inf`). Come puoi notare, è nei frames precedenti che occorre esaminare quando i numeri iniziano a diventare molto grandi per i valori fp16. Confrontiamo il report al codice `models/t5/modeling_t5.py`: ```python class T5DenseGatedGeluDense(nn.Module): def __init__(self, config): super().__init__() self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) self.dropout = nn.Dropout(config.dropout_rate) self.gelu_act = ACT2FN["gelu_new"] def forward(self, hidden_states): hidden_gelu = self.gelu_act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states ``` Ora è facile vedere la chiamata `dropout`, e tutte le chiamate precedenti. Poiché il rilevamento avviene in un avanzamento (forward hook in eng.), i rapporti vengono creati immeditamente dopo ogni rientro da `forward` (forward returns in eng.). Tornando al rapporto completo, per agire e risolvere il problema, dobbiamo andare qualche frame più in alto, dove i numeri hanno iniziato a salire, e probabilmente passare alla modalità `fp32`, in modo che i numeri non trabocchino quando vengono moltiplicati o sommati. Naturalmente, potrebbero esserci altre soluzioni. Per esempio, potremmo spegnere temporanemante `amp` se è abilitato, successivamente spostare `forward` in un helper wrapper, come: ```python def _forward(self, hidden_states): hidden_gelu = self.gelu_act(self.wi_0(hidden_states)) hidden_linear = self.wi_1(hidden_states) hidden_states = hidden_gelu * hidden_linear hidden_states = self.dropout(hidden_states) hidden_states = self.wo(hidden_states) return hidden_states import torch def forward(self, hidden_states): if torch.is_autocast_enabled(): with torch.cuda.amp.autocast(enabled=False): return self._forward(hidden_states) else: return self._forward(hidden_states) ``` Poiché il rilevatore automatico riporta solo gli ingressi e le uscite di fotogrammi completi, una volta che si sa dove cercare, si può analizzare anche le fasi intermedie di una specifica funzione `forward`. In alcuni casi puoi usare la funzione di supporto `detect_overflow` per indirizzare il rilevatore dove preferisci, ad esempio: ```python from debug_utils import detect_overflow class T5LayerFF(nn.Module): [...] def forward(self, hidden_states): forwarded_states = self.layer_norm(hidden_states) detect_overflow(forwarded_states, "after layer_norm") forwarded_states = self.DenseReluDense(forwarded_states) detect_overflow(forwarded_states, "after DenseReluDense") return hidden_states + self.dropout(forwarded_states) ``` Si può vedere che abbiamo aggiunto 2 di questi e ora teniamo traccia se `inf` o `nan` per `forwarded_states` è stato rilevato da qualche parte. In realtà, il rilevatore li riporta già, perché ciascuna delle chiamate nell'esempio precedente è un `nn.Module`, ma diciamo che se avessimo dei calcoli diretti locali, questo è il modo in cui lo faremmo. Inoltre, se si istanzia il debugger nel proprio codice, è possibile modificare il numero di fotogrammi stampati rispetto a predefinito, ad esempio.: ```python from .debug_utils import DebugUnderflowOverflow debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100) ``` ### Tracciamento della mistura assoluta del lotto specifico e del valore massimo La stessa classe di debug può essere utilizzata per il tracciamento per-batch con la funzione di rilevamento di underflow/overflow disattivata. Supponiamo di voler osservare i valori minimi e massimi assoluti per tutti gli ingredienti di ogni chiamata `forward` di un dato lotto. lotto, e che lo si voglia fare solo per i lotti 1 e 3. Si istanzia questa classe come: ```python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3]) ``` Ora i batch completi 1 e 3 saranno tracciati utilizzando lo stesso formato del rilevatore di underflow/overflow. I batches sono 0-indexed. Questo è utile se si sa che il programma inizia a comportarsi male dopo un certo numero di batch, in modo da poter avanzare velocemente fino a quell'area. direttamente a quell'area. Ecco un esempio di output troncato per questa configurazione: ``` *** Starting batch number=1 *** abs min abs max metadata shared Embedding 1.01e-06 7.92e+02 weight 0.00e+00 2.47e+04 input[0] 5.36e-05 7.92e+02 output [...] decoder.dropout Dropout 1.60e-07 2.27e+01 input[0] 0.00e+00 2.52e+01 output decoder T5Stack not a tensor output lm_head Linear 1.01e-06 7.92e+02 weight 0.00e+00 1.11e+00 input[0] 6.06e-02 8.39e+01 output T5ForConditionalGeneration not a tensor output *** Starting batch number=3 *** abs min abs max metadata shared Embedding 1.01e-06 7.92e+02 weight 0.00e+00 2.78e+04 input[0] 5.36e-05 7.92e+02 output [...] ``` Qui verrà scaricato un numero enorme di fotogrammi, tanti quanti sono le chiamate in avanti nel modello, quindi può essere o non essere quello che volete, ma a volte può essere più utile usarlo di un classico debugger. Per esempio, se il problema inizia a verificarsi a partire dal lotto numero 150. Quindi è possibile scaricare le tracce dei lotti 149 e 150 e confrontare i punti in cui i numeri hanno iniziato a divergere. È inoltre possibile specificare il numero di batch dopo il quale interrompere l'addestramento, con: ```python debug_overflow = DebugUnderflowOverflow(model, trace_batch_nums=[1, 3], abort_after_batch_num=3) ```

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# Controlli su una Pull Request Quando apri una pull request sui 🤗 Transformers, vengono eseguiti un discreto numero di controlli per assicurarsi che la patch che stai aggiungendo non stia rompendo qualcosa di esistente. Questi controlli sono di quattro tipi: - test regolari - costruzione della documentazione - stile del codice e della documentazione - coerenza generale del repository In questo documento, cercheremo di spiegare quali sono i vari controlli e le loro ragioni, oltre a spiegare come eseguire il debug locale se uno di essi fallisce sulla tua PR. Nota che tutti richiedono un'installazione dev: ```bash pip install transformers[dev] ``` o un'installazione modificabile: ```bash pip install -e .[dev] ``` all'interno del repo Transformers. ## Tests Tutti i job che iniziano con `ci/circleci: run_tests_` eseguono parti della suite di test dei Transformers. Ognuno di questi job si concentra su una parte della libreria in un determinato ambiente: per esempio `ci/circleci: run_tests_pipelines_tf` esegue il test delle pipeline in un ambiente in cui è installato solo TensorFlow. Nota che per evitare di eseguire i test quando non ci sono cambiamenti reali nei moduli che si stanno testando, ogni volta viene eseguita solo una parte della suite di test: viene eseguita una utility per determinare le differenze nella libreria tra prima e dopo la PR (ciò che GitHub mostra nella scheda "Files changes") e sceglie i test che sono stati impattati dalla diff. Questa utility può essere eseguita localmente con: ```bash python utils/tests_fetcher.py ``` dalla root del repo Transformers. Di seguito ciò che farà: 1. Controlla per ogni file nel diff se le modifiche sono nel codice o solo nei commenti o nelle docstrings. Vengono mantenuti solo i file con modifiche reali al codice. 2. Costruisce una mappa interna che fornisce per ogni file del codice sorgente della libreria tutti i file su cui ha un impatto ricorsivo. Si dice che il modulo A ha un impatto sul modulo B se il modulo B importa il modulo A. Per l'impatto ricorsivo, abbiamo bisogno di una catena di moduli che va dal modulo A al modulo B in cui ogni modulo importa il precedente. 3. Applica questa mappa ai file raccolti nel passaggio 1, si ottiene l'elenco dei file del modello interessati dalla PR. 4. Mappa ciascuno di questi file con i corrispondenti file di test e ottiene l'elenco dei test da eseguire. Quando esegui lo script in locale, dovresti ottenere la stampa dei risultati dei passi 1, 3 e 4 e quindi sapere quali test sono stati eseguiti. Lo script creerà anche un file chiamato `test_list.txt` che contiene l'elenco dei test da eseguire e che puoi eseguire localmente con il seguente comando: ```bash python -m pytest -n 8 --dist=loadfile -rA -s $(cat test_list.txt) ``` Nel caso in cui qualcosa sia sfuggito, l'intera suite di test viene eseguita quotidianamente. ## Build della documentazione Il job `ci/circleci: build_doc` esegue una build della documentazione per assicurarsi che tutto sia a posto una volta che la PR è stata unita. Se questo passaggio fallisce, puoi controllare localmente entrando nella cartella `docs` del repo Transformers e digitare ```bash make html ``` Sphinx non è noto per i suoi messaggi di errore chiari, quindi potrebbe essere necessario che provi alcune cose per trovare davvero la fonte dell'errore. ## Stile del codice e della documentazione La formattazione del codice viene applicata a tutti i file sorgenti, agli esempi e ai test usando `black` e `isort`. Abbiamo anche uno strumento personalizzato che si occupa della formattazione delle docstring e dei file `rst` (`utils/style_doc.py`), così come dell'ordine dei lazy imports eseguiti nei file `__init__.py` dei Transformers (`utils/custom_init_isort.py`). Tutto questo può essere lanciato eseguendo ```bash make style ``` I controlli della CI sono applicati all'interno del controllo `ci/circleci: check_code_quality`. Esegue anche `flake8`, che dà un'occhiata di base al codice e si lamenta se trova una variabile non definita o non utilizzata. Per eseguire questo controllo localmente, usare ```bash make quality ``` Questa operazione può richiedere molto tempo, quindi per eseguire la stessa operazione solo sui file modificati nel branch corrente, eseguire ```bash make fixup ``` Quest'ultimo comando eseguirà anche tutti i controlli aggiuntivi per la consistenza del repository. Diamogli un'occhiata. ## Coerenza del repository All'interno sono raggruppati tutti i test per assicurarsi che la tua PR lasci il repository in un buono stato ed è eseguito dal controllo `ci/circleci: check_repository_consistency`. Puoi eseguire localmente questo controllo eseguendo quanto segue: ```bash make repo-consistency ``` Questo verifica che: - Tutti gli oggetti aggiunti all'init sono documentati (eseguito da `utils/check_repo.py`) - Tutti i file `__init__.py` hanno lo stesso contenuto nelle loro due sezioni (eseguito da `utils/check_inits.py`) - Tutto il codice identificato come copia da un altro modulo è coerente con l'originale (eseguito da `utils/check_copies.py`) - Le traduzioni dei README e l'indice della documentazione hanno lo stesso elenco di modelli del README principale (eseguito da `utils/check_copies.py`) - Le tabelle autogenerate nella documentazione sono aggiornate (eseguito da `utils/check_table.py`) - La libreria ha tutti gli oggetti disponibili anche se non tutte le dipendenze opzionali sono installate (eseguito da `utils/check_dummies.py`) Se questo controllo fallisce, le prime due voci richiedono una correzione manuale, mentre le ultime quattro possono essere corrette automaticamente per te eseguendo il comando ```bash make fix-copies ``` Ulteriori controlli riguardano le PR che aggiungono nuovi modelli, principalmente che: - Tutti i modelli aggiunti sono in un Auto-mapping (eseguita da `utils/check_repo.py`)  - Tutti i modelli sono testati correttamente (eseguito da `utils/check_repo.py`)

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# BARTpho ## Overview BARTpho モデルは、Nguyen Luong Tran、Duong Minh Le、Dat Quoc Nguyen によって [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnam](https://arxiv.org/abs/2109.09701) で提案されました。論文の要約は次のとおりです。 *BARTpho には、BARTpho_word と BARTpho_syllable の 2 つのバージョンがあり、初の公開された大規模な単一言語です。ベトナム語用に事前トレーニングされたシーケンスツーシーケンスモデル。当社の BARTpho は「大規模な」アーキテクチャと事前トレーニングを使用しますシーケンス間ノイズ除去モデル BART のスキームなので、生成 NLP タスクに特に適しています。実験ベトナム語テキスト要約の下流タスクでは、自動評価と人間による評価の両方で、BARTpho が強力なベースライン mBART を上回り、最先端の性能を向上させます。将来を容易にするためにBARTphoをリリースします生成的なベトナム語 NLP タスクの研究と応用。* このモデルは [dqnguyen](https://huggingface.co/dqnguyen) によって提供されました。元のコードは [こちら](https://github.com/VinAIResearch/BARTpho) にあります。 ## Usage example ```python >>> import torch >>> from transformers import AutoModel, AutoTokenizer >>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable") >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable") >>> line = "Chúng tôi là những nghiên cứu viên." >>> input_ids = tokenizer(line, return_tensors="pt") >>> with torch.no_grad(): ... features = bartpho(**input_ids) # Models outputs are now tuples >>> # With TensorFlow 2.0+: >>> from transformers import TFAutoModel >>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable") >>> input_ids = tokenizer(line, return_tensors="tf") >>> features = bartpho(**input_ids) ``` ## Usage tips - mBARTに続いて、BARTphoはBARTの「大規模な」アーキテクチャを使用し、その上に追加の層正規化層を備えています。エンコーダとデコーダの両方。したがって、[BART のドキュメント](bart) の使用例は、使用に適応する場合に使用されます。 BARTpho を使用する場合は、BART に特化したクラスを mBART に特化した対応するクラスに置き換えることによって調整する必要があります。例えば： ```python >>> from transformers import MBartForConditionalGeneration >>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable") >>> TXT = "Chúng tôi là <mask> nghiên cứu viên." >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"] >>> logits = bartpho(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() >>> probs = logits[0, masked_index].softmax(dim=0) >>> values, predictions = probs.topk(5) >>> print(tokenizer.decode(predictions).split()) ``` - この実装はトークン化のみを目的としています。`monolingual_vocab_file`はベトナム語に特化した型で構成されています多言語 XLM-RoBERTa から利用できる事前トレーニング済み SentencePiece モデル`vocab_file`から抽出されます。他の言語 (サブワードにこの事前トレーニング済み多言語 SentencePiece モデル`vocab_file`を使用する場合) セグメンテーションにより、独自の言語に特化した`monolingual_vocab_file`を使用して BartphoTokenizer を再利用できます。 ## BartphoTokenizer [[autodoc]] BartphoTokenizer

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# BridgeTower ## Overview BridgeTower モデルは、Xiao Xu、Chenfei Wu、Shachar Rosenman、Vasudev Lal、Wanxiang Che、Nan Duan [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://arxiv.org/abs/2206.08657) で提案されました。ドゥアン。このモデルの目標は、各ユニモーダルエンコーダとクロスモーダルエンコーダの間のブリッジにより、クロスモーダルエンコーダの各層での包括的かつ詳細な対話が可能になり、追加のパフォーマンスと計算コストがほとんど無視できる程度で、さまざまな下流タスクで優れたパフォーマンスを実現します。この論文は [AAAI'23](https://aaai.org/Conferences/AAAI-23/) 会議に採択されました。論文の要約は次のとおりです。 *TWO-TOWER アーキテクチャを備えたビジョン言語 (VL) モデルは、近年の視覚言語表現学習の主流となっています。現在の VL モデルは、軽量のユニモーダルエンコーダーを使用して、ディープクロスモーダルエンコーダーで両方のモダリティを同時に抽出、位置合わせ、融合することを学習するか、事前にトレーニングされたディープユニモーダルエンコーダーから最終層のユニモーダル表現を上部のクロスモーダルエンコーダー。どちらのアプローチも、視覚言語表現の学習を制限し、モデルのパフォーマンスを制限する可能性があります。この論文では、ユニモーダルエンコーダの最上位層とクロスモーダルエンコーダの各層の間の接続を構築する複数のブリッジ層を導入する BRIDGETOWER を提案します。これにより、効果的なボトムアップのクロスモーダル調整と、クロスモーダルエンコーダー内の事前トレーニング済みユニモーダルエンコーダーのさまざまなセマンティックレベルの視覚表現とテキスト表現の間の融合が可能になります。 BRIDGETOWER は 4M 画像のみで事前トレーニングされており、さまざまな下流の視覚言語タスクで最先端のパフォーマンスを実現します。特に、VQAv2 テスト標準セットでは、BRIDGETOWER は 78.73% の精度を達成し、同じ事前トレーニングデータとほぼ無視できる追加パラメータと計算コストで以前の最先端モデル METER を 1.09% 上回りました。特に、モデルをさらにスケーリングすると、BRIDGETOWER は 81.15% の精度を達成し、桁違いに大きなデータセットで事前トレーニングされたモデルを上回りました。* <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/bridgetower_architecture%20.jpg" alt="drawing" width="600"/> <small> ブリッジタワーアーキテクチャ。 <a href="https://arxiv.org/abs/2206.08657">元の論文から抜粋。</a> </small> このモデルは、[Anahita Bhiwandiwalla](https://huggingface.co/anahita-b)、[Tiep Le](https://huggingface.co/Tile)、[Shaoyen Tseng](https://huggingface.co/shaoyent) 。元のコードは [ここ](https://github.com/microsoft/BridgeTower) にあります。 ## Usage tips and examples BridgeTower は、ビジュアルエンコーダー、テキストエンコーダー、および複数の軽量ブリッジレイヤーを備えたクロスモーダルエンコーダーで構成されます。このアプローチの目標は、各ユニモーダルエンコーダーとクロスモーダルエンコーダーの間にブリッジを構築し、クロスモーダルエンコーダーの各層で包括的かつ詳細な対話を可能にすることでした。原則として、提案されたアーキテクチャでは、任意のビジュアル、テキスト、またはクロスモーダルエンコーダを適用できます。 [`BridgeTowerProcessor`] は、[`RobertaTokenizer`] と [`BridgeTowerImageProcessor`] を単一のインスタンスにラップし、両方の機能を実現します。テキストをエンコードし、画像をそれぞれ用意します。次の例は、[`BridgeTowerProcessor`] と [`BridgeTowerForContrastiveLearning`] を使用して対照学習を実行する方法を示しています。 ```python >>> from transformers import BridgeTowerProcessor, BridgeTowerForContrastiveLearning >>> import requests >>> from PIL import Image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"] >>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc") >>> model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc") >>> # forward pass >>> scores = dict() >>> for text in texts: ... # prepare inputs ... encoding = processor(image, text, return_tensors="pt") ... outputs = model(**encoding) ... scores[text] = outputs ``` 次の例は、[`BridgeTowerProcessor`] と [`BridgeTowerForImageAndTextRetrieval`] を使用して画像テキストの取得を実行する方法を示しています。 ```python >>> from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval >>> import requests >>> from PIL import Image >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"] >>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm") >>> model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm") >>> # forward pass >>> scores = dict() >>> for text in texts: ... # prepare inputs ... encoding = processor(image, text, return_tensors="pt") ... outputs = model(**encoding) ... scores[text] = outputs.logits[0, 1].item() ``` 次の例は、[`BridgeTowerProcessor`] と [`BridgeTowerForMaskedLM`] を使用してマスクされた言語モデリングを実行する方法を示しています。 ```python >>> from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000360943.jpg" >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") >>> text = "a <mask> looking out of the window" >>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm") >>> model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm") >>> # prepare inputs >>> encoding = processor(image, text, return_tensors="pt") >>> # forward pass >>> outputs = model(**encoding) >>> results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist()) >>> print(results) .a cat looking out of the window. ``` チップ： - BridgeTower のこの実装では、[`RobertaTokenizer`] を使用してテキスト埋め込みを生成し、OpenAI の CLIP/ViT モデルを使用して視覚的埋め込みを計算します。 - 事前トレーニングされた [bridgeTower-base](https://huggingface.co/BridgeTower/bridgetower-base) および [bridgetower マスクされた言語モデリングと画像テキストマッチング](https://huggingface.co/BridgeTower/bridgetower--base-itm-mlm) のチェックポイントがリリースされました。 - 画像検索およびその他の下流タスクにおける BridgeTower のパフォーマンスについては、[表 5](https://arxiv.org/pdf/2206.08657.pdf) を参照してください。 - このモデルの PyTorch バージョンは、torch 1.10 以降でのみ使用できます。 ## BridgeTowerConfig [[autodoc]] BridgeTowerConfig ## BridgeTowerTextConfig [[autodoc]] BridgeTowerTextConfig ## BridgeTowerVisionConfig [[autodoc]] BridgeTowerVisionConfig ## BridgeTowerImageProcessor [[autodoc]] BridgeTowerImageProcessor - preprocess ## BridgeTowerProcessor [[autodoc]] BridgeTowerProcessor - __call__ ## BridgeTowerModel [[autodoc]] BridgeTowerModel - forward ## BridgeTowerForContrastiveLearning [[autodoc]] BridgeTowerForContrastiveLearning - forward ## BridgeTowerForMaskedLM [[autodoc]] BridgeTowerForMaskedLM - forward ## BridgeTowerForImageAndTextRetrieval [[autodoc]] BridgeTowerForImageAndTextRetrieval - forward

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# CPM ## Overview CPM モデルは、Zhengyan Zhang、Xu Han、Hao Zhou、Pei Ke、Yuxian Gu によって [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) で提案されました。葉徳明、秦裕佳、 Yusheng Su、Haozhe Ji、Jian Guan、Fanchao Qi、Xiaozi Wang、Yanan Zheng、Guoyang Zeng、Huanqi Cao、Shengqi Chen、 Daixuan Li、Zhenbo Sun、Zhiyuan Liu、Minlie Huang、Wentao Han、Jie Tang、Juanzi Li、Xiaoyan Zhu、Maosong Sun。論文の要約は次のとおりです。 *事前トレーニングされた言語モデル (PLM) は、さまざまな下流の NLP タスクに有益であることが証明されています。最近ではGPT-3、 1,750億個のパラメータと570GBの学習データを備え、数回の撮影（1枚でも）の容量で大きな注目を集めましたゼロショット）学習。ただし、GPT-3 を適用して中国語の NLP タスクに対処することは依然として困難です。 GPT-3 の言語は主に英語であり、パラメーターは公開されていません。この技術レポートでは、大規模な中国語トレーニングデータに対する生成的事前トレーニングを備えた中国語事前トレーニング済み言語モデル (CPM)。最高に私たちの知識の限りでは、26 億のパラメータと 100GB の中国語トレーニングデータを備えた CPM は、事前トレーニングされた中国語としては最大のものです。言語モデルは、会話、エッセイの作成、クローゼテストと言語理解。広範な実験により、CPM が多くの環境で優れたパフォーマンスを達成できることが実証されています。少数ショット (ゼロショットでも) 学習の設定での NLP タスク。* このモデルは [canwenxu](https://huggingface.co/canwenxu) によって提供されました。オリジナルの実装が見つかりますここ: https://github.com/TsinghuaAI/CPM-Generate <Tip> CPM のアーキテクチャは、トークン化方法を除いて GPT-2 と同じです。詳細については、[GPT-2 ドキュメント](openai-community/gpt2) を参照してください。 API リファレンス情報。 </Tip> ## CpmTokenizer [[autodoc]] CpmTokenizer ## CpmTokenizerFast [[autodoc]] CpmTokenizerFast

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# Share a Model 最後の2つのチュートリアルでは、PyTorch、Keras、および🤗 Accelerateを使用してモデルをファインチューニングする方法を示しました。次のステップは、モデルをコミュニティと共有することです！Hugging Faceでは、知識とリソースを公開的に共有し、人工知能を誰にでも提供することを信じています。他の人々が時間とリソースを節約できるように、モデルをコミュニティと共有することを検討することをお勧めします。このチュートリアルでは、訓練済みまたはファインチューニングされたモデルを[Model Hub](https://huggingface.co/models)に共有する2つの方法を学びます： - プログラムでファイルをHubにプッシュする。 - ウェブインターフェースを使用してファイルをHubにドラッグアンドドロップする。 <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> コミュニティとモデルを共有するには、[huggingface.co](https://huggingface.co/join)でアカウントが必要です。既存の組織に参加したり、新しい組織を作成したりすることもできます。 </Tip> ## Repository Features Model Hub上の各リポジトリは、通常のGitHubリポジトリのように動作します。リポジトリはバージョニング、コミット履歴、違いの視覚化の機能を提供します。 Model Hubの組み込みバージョニングはgitおよび[git-lfs](https://git-lfs.github.com/)に基づいています。言い換えれば、モデルを1つのリポジトリとして扱うことができ、より大きなアクセス制御とスケーラビリティを実現します。バージョン管理には*リビジョン*があり、コミットハッシュ、タグ、またはブランチ名で特定のモデルバージョンをピン留めする方法です。その結果、`revision`パラメータを使用して特定のモデルバージョンをロードできます： ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # タグ名、またはブランチ名、またはコミットハッシュ ... ) ``` ファイルはリポジトリ内で簡単に編集でき、コミット履歴と差分を表示できます： ![vis_diff](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png) ## Set Up モデルをHubに共有する前に、Hugging Faceの認証情報が必要です。ターミナルへのアクセス権がある場合、🤗 Transformersがインストールされている仮想環境で以下のコマンドを実行します。これにより、アクセストークンがHugging Faceのキャッシュフォルダに保存されます（デフォルトでは `~/.cache/` に保存されます）： ```bash huggingface-cli login ``` JupyterやColaboratoryのようなノートブックを使用している場合、[`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library)ライブラリがインストールされていることを確認してください。このライブラリを使用すると、Hubとプログラム的に対話できます。 ```bash pip install huggingface_hub ``` 次に、`notebook_login`を使用してHubにサインインし、[こちらのリンク](https://huggingface.co/settings/token)にアクセスしてログインに使用するトークンを生成します： ```python >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Convert a Model for all frameworks 異なるフレームワークで作業している他のユーザーがあなたのモデルを使用できるようにするために、 PyTorchおよびTensorFlowのチェックポイントでモデルを変換してアップロードすることをお勧めします。このステップをスキップすると、ユーザーは異なるフレームワークからモデルをロードできますが、モデルをオンザフライで変換する必要があるため、遅くなります。別のフレームワーク用にチェックポイントを変換することは簡単です。 PyTorchとTensorFlowがインストールされていることを確認してください（インストール手順については[こちら](installation)を参照）し、その後、他のフレームワーク向けに特定のタスク用のモデルを見つけます。 <frameworkcontent> <pt> TensorFlowからPyTorchにチェックポイントを変換するには、`from_tf=True`を指定します： ```python >>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True) >>> pt_model.save_pretrained("path/to/awesome-name-you-picked") ``` </pt> <tf> 指定して、PyTorchからTensorFlowにチェックポイントを変換するには `from_pt=True` を使用します： ```python >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True) ``` 新しいTensorFlowモデルとその新しいチェックポイントを保存できます： ```python >>> tf_model.save_pretrained("path/to/awesome-name-you-picked") ``` </tf> <tf> <jax> Flaxでモデルが利用可能な場合、PyTorchからFlaxへのチェックポイントの変換も行うことができます： ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/to/awesome-name-you-picked", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Push a model during traning <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> モデルをHubにプッシュすることは、追加のパラメーターまたはコールバックを追加するだけで簡単です。 [ファインチューニングチュートリアル](training)から思い出してください、[`TrainingArguments`]クラスはハイパーパラメーターと追加のトレーニングオプションを指定する場所です。これらのトレーニングオプションの1つに、モデルを直接Hubにプッシュする機能があります。[`TrainingArguments`]で`push_to_hub=True`を設定します： ```py >>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True) ``` Pass your training arguments as usual to [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` [`Trainer`]に通常通りトレーニング引数を渡します： ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` ファインチューニングが完了したら、[`Trainer`]で[`~transformers.Trainer.push_to_hub`]を呼び出して、トレーニング済みモデルをHubにプッシュします。🤗 Transformersは、トレーニングのハイパーパラメータ、トレーニング結果、およびフレームワークのバージョンを自動的にモデルカードに追加します！ ```py >>> trainer.push_to_hub() ``` </pt> <tf> [`PushToHubCallback`]を使用してモデルをHubに共有します。[`PushToHubCallback`]関数には、次のものを追加します： - モデルの出力ディレクトリ。 - トークナイザ。 - `hub_model_id`、つまりHubのユーザー名とモデル名。 ```python >>> from transformers import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model" ... ) ``` 🤗 Transformersは[`fit`](https://keras.io/api/models/model_training_apis/)にコールバックを追加し、トレーニング済みモデルをHubにプッシュします： ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## `push_to_hub` 関数を使用するまた、モデルを直接Hubにアップロードするために、`push_to_hub` を呼び出すこともできます。 `push_to_hub` でモデル名を指定します： ```py >>> pt_model.push_to_hub("my-awesome-model") ``` これにより、ユーザー名の下にモデル名 `my-awesome-model` を持つリポジトリが作成されます。ユーザーは、`from_pretrained` 関数を使用してモデルをロードできます： ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("your_username/my-awesome-model") ``` 組織に所属し、モデルを組織名のもとにプッシュしたい場合、`repo_id` にそれを追加してください： ```python >>> pt_model.push_to_hub("my-awesome-org/my-awesome-model") ``` `push_to_hub`関数は、モデルリポジトリに他のファイルを追加するためにも使用できます。例えば、トークナイザをモデルリポジトリに追加します： ```py >>> tokenizer.push_to_hub("my-awesome-model") ``` あるいは、ファインチューニングされたPyTorchモデルのTensorFlowバージョンを追加したいかもしれません： ```python >>> tf_model.push_to_hub("my-awesome-model") ``` Hugging Faceプロフィールに移動すると、新しく作成したモデルリポジトリが表示されるはずです。**Files**タブをクリックすると、リポジトリにアップロードしたすべてのファイルが表示されます。リポジトリにファイルを作成およびアップロードする方法の詳細については、Hubドキュメンテーション[こちら](https://huggingface.co/docs/hub/how-to-upstream)を参照してください。 ## Upload with the web interface コードを書かずにモデルをアップロードしたいユーザーは、Hubのウェブインターフェースを使用してモデルをアップロードできます。[huggingface.co/new](https://huggingface.co/new)を訪れて新しいリポジトリを作成します： ![new_model_repo](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png) ここから、モデルに関するいくつかの情報を追加します： - リポジトリの**所有者**を選択します。これはあなた自身または所属している組織のいずれかです。 - モデルの名前を選択します。これはリポジトリの名前にもなります。 - モデルが公開か非公開かを選択します。 - モデルのライセンス使用方法を指定します。その後、**Files**タブをクリックし、**Add file**ボタンをクリックしてリポジトリに新しいファイルをアップロードします。次に、ファイルをドラッグアンドドロップしてアップロードし、コミットメッセージを追加します。 ![upload_file](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png) ## Add a model card ユーザーがモデルの機能、制限、潜在的な偏り、倫理的な考慮事項を理解できるようにするために、モデルリポジトリにモデルカードを追加してください。モデルカードは`README.md`ファイルで定義されます。モデルカードを追加する方法： * 手動で`README.md`ファイルを作成およびアップロードする。 * モデルリポジトリ内の**Edit model card**ボタンをクリックする。モデルカードに含めるべき情報の例については、DistilBert [モデルカード](https://huggingface.co/distilbert/distilbert-base-uncased)をご覧ください。`README.md`ファイルで制御できる他のオプション、例えばモデルの炭素フットプリントやウィジェットの例などについての詳細は、[こちらのドキュメンテーション](https://huggingface.co/docs/hub/models-cards)を参照してください。

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# Image tasks with IDEFICS [[open-in-colab]] 個別のタスクは特殊なモデルを微調整することで対処できますが、別のアプローチも可能です。最近登場して人気を博しているのは、微調整を行わずにさまざまなタスクに大規模なモデルを使用することです。たとえば、大規模な言語モデルは、要約、翻訳、分類などの NLP タスクを処理できます。このアプローチは、テキストなどの単一のモダリティに限定されなくなりました。このガイドでは、次のような方法を説明します。 IDEFICS と呼ばれる大規模なマルチモーダルモデルを使用して、画像とテキストのタスクを解決します。 [IDEFICS](../model_doc/idefics) は、[Flamingo](https://huggingface.co/papers/2204.14198) に基づくオープンアクセスのビジョンおよび言語モデルです。 DeepMind によって最初に開発された最先端の視覚言語モデル。モデルは任意の画像シーケンスを受け入れますテキストを入力し、出力として一貫したテキストを生成します。画像に関する質問に答えたり、視覚的なコンテンツについて説明したり、複数のイメージに基づいたストーリーを作成するなど。 IDEFICS には 2 つのバリエーションがあります - [800 億パラメータ](https://huggingface.co/HuggingFaceM4/idefics-80b) および [90 億のパラメータ](https://huggingface.co/HuggingFaceM4/idefics-9b)、どちらも 🤗 Hub で入手できます。各バリエーションについて、細かく調整された指示も見つけることができます。会話のユースケースに適応したモデルのバージョン。このモデルは非常に多用途で、幅広い画像タスクやマルチモーダルタスクに使用できます。しかし、大規模なモデルであるということは、大量の計算リソースとインフラストラクチャが必要であることを意味します。それはあなた次第ですこのアプローチは、個別のタスクごとに特化したモデルを微調整するよりも、ユースケースに適しています。このガイドでは、次の方法を学習します。 - [IDEFICS をロード](#loading-the-model) および [モデルの量子化バージョンをロード](#quantized-model) - IDEFICS を次の目的で使用します。 - [画像キャプション](#image-captioning) - [プロンプト画像キャプション](#prompted-image-captioning) - [Few-shot プロンプト](#few-shot-prompting) - [ビジュアル質問回答](#visual-question-answering) - [画像分類](#image-classification) - [画像ガイド付きテキスト生成](#image-guided-text-generation) - [バッチモードで推論を実行する](#running-inference-in-batch-mode) - [会話用に IDEFICS 命令を実行](#idefics-instruct-for-conversational-use) 始める前に、必要なライブラリがすべてインストールされていることを確認してください。 ```bash pip install -q bitsandbytes sentencepiece accelerate transformers ``` <Tip> 量子化されていないバージョンのモデルチェックポイントを使用して次の例を実行するには、少なくとも 20GB の GPU メモリが必要です。 </Tip> ## Loading the model まずはモデルの 90 億個のパラメーターのチェックポイントをロードしましょう。 ```py >>> checkpoint = "HuggingFaceM4/idefics-9b" ``` 他の Transformers モデルと同様に、プロセッサとモデル自体をチェックポイントからロードする必要があります。 IDEFICS プロセッサは、[`LlamaTokenizer`] と IDEFICS 画像プロセッサを単一のプロセッサにラップして処理します。モデルのテキストと画像の入力を準備します。 ```py >>> import torch >>> from transformers import IdeficsForVisionText2Text, AutoProcessor >>> processor = AutoProcessor.from_pretrained(checkpoint) >>> model = IdeficsForVisionText2Text.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, device_map="auto") ``` `device_map`を`auto`に設定すると、モデルの重みを最も最適化された状態でロードおよび保存する方法が自動的に決定されます。既存のデバイスを考慮した方法。 ### Quantized model ハイメモリ GPU の可用性が問題となる場合は、モデルの量子化されたバージョンをロードできます。モデルとプロセッサを 4 ビット精度で使用する場合、`BitsAndBytesConfig`を`from_pretrained`メソッドに渡すと、モデルが圧縮されます。ロード中にその場で。 ```py >>> import torch >>> from transformers import IdeficsForVisionText2Text, AutoProcessor, BitsAndBytesConfig >>> quantization_config = BitsAndBytesConfig( ... load_in_4bit=True, ... bnb_4bit_compute_dtype=torch.float16, ... ) >>> processor = AutoProcessor.from_pretrained(checkpoint) >>> model = IdeficsForVisionText2Text.from_pretrained( ... checkpoint, ... quantization_config=quantization_config, ... device_map="auto" ... ) ``` 提案された方法のいずれかでモデルをロードしたので、IDEFICS を使用できるタスクの探索に進みましょう。 ## Image captioning 画像のキャプション付けは、特定の画像のキャプションを予測するタスクです。一般的な用途は視覚障害者を支援することです人々はさまざまな状況をナビゲートします。たとえば、オンラインで画像コンテンツを探索します。タスクを説明するには、キャプションを付ける画像を取得します。例: <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-im-captioning.jpg" alt="Image of a puppy in a flower bed"/> </div> 写真提供：[Hendo Wang](https://unsplash.com/@hendoo) IDEFICS はテキストと画像のプロンプトを受け入れます。ただし、画像にキャプションを付けるには、テキストプロンプトをユーザーに提供する必要はありません。モデル、前処理された入力画像のみ。テキストプロンプトがない場合、モデルはテキストの生成を開始します。 BOS (Beginning-of-sequence) トークンによりキャプションが作成されます。モデルへの画像入力として、画像オブジェクト (`PIL.Image`) または画像を取得できる URL のいずれかを使用できます。 ```py >>> prompt = [ ... "https://images.unsplash.com/photo-1583160247711-2191776b4b91?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3542&q=80", ... ] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=10, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) A puppy in a flower bed ``` <Tip> 増加時に発生するエラーを避けるために、`generate`の呼び出しに`bad_words_ids`を含めることをお勧めします。 `max_new_tokens`: モデルは、新しい `<image>` または `<fake_token_around_image>` トークンを生成する必要があります。モデルによって画像が生成されていません。このガイドのようにオンザフライで設定することも、[テキスト生成戦略](../generation_strategies) ガイドで説明されているように `GenerationConfig` に保存することもできます。 </Tip> ## Prompted image captioning テキストプロンプトを提供することで画像キャプションを拡張でき、モデルは画像を指定して続行します。持っていきましょう別の図で説明します。 <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-prompted-im-captioning.jpg" alt="Image of the Eiffel Tower at night"/> </div> 写真提供：[Denys Nevozhai](https://unsplash.com/@dnevozhai)。テキストおよび画像のプロンプトを単一のリストとしてモデルのプロセッサに渡し、適切な入力を作成できます。 ```py >>> prompt = [ ... "https://images.unsplash.com/photo-1543349689-9a4d426bee8e?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3501&q=80", ... "This is an image of ", ... ] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=10, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) This is an image of the Eiffel Tower in Paris, France. ``` ## Few-shot prompting IDEFICS はゼロショットで優れた結果を示しますが、タスクによっては特定の形式のキャプションが必要になる場合や、キャプションが付属する場合があります。タスクの複雑さを増大させるその他の制限または要件。少数のショットのプロンプトを使用して、コンテキスト内の学習を有効にすることができます。プロンプトに例を指定することで、指定された例の形式を模倣した結果を生成するようにモデルを操作できます。前のエッフェル塔の画像をモデルの例として使用し、モデルにデモンストレーションするプロンプトを作成してみましょう。画像内のオブジェクトが何であるかを知ることに加えて、それに関する興味深い情報も取得したいと考えています。次に、自由の女神の画像に対して同じ応答形式を取得できるかどうかを見てみましょう。 <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg" alt="Image of the Statue of Liberty"/> </div> 写真提供：[Juan Mayobre](https://unsplash.com/@jmayobres)。 ```py >>> prompt = ["User:", ... "https://images.unsplash.com/photo-1543349689-9a4d426bee8e?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3501&q=80", ... "Describe this image.\nAssistant: An image of the Eiffel Tower at night. Fun fact: the Eiffel Tower is the same height as an 81-storey building.\n", ... "User:", ... "https://images.unsplash.com/photo-1524099163253-32b7f0256868?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3387&q=80", ... "Describe this image.\nAssistant:" ... ] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=30, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) User: Describe this image. Assistant: An image of the Eiffel Tower at night. Fun fact: the Eiffel Tower is the same height as an 81-storey building. User: Describe this image. Assistant: An image of the Statue of Liberty. Fun fact: the Statue of Liberty is 151 feet tall. ``` モデルは 1 つの例 (つまり、1 ショット) だけからタスクの実行方法を学習していることに注目してください。より複雑なタスクの場合は、より多くの例 (3 ショット、5 ショットなど) を自由に試してみてください。 ## Visual question answering Visual Question Answering (VQA) は、画像に基づいて自由形式の質問に答えるタスクです。画像に似ているキャプションは、アクセシビリティアプリケーションだけでなく、教育 (視覚資料についての推論) にも使用できます。サービス（画像を基にした商品に関する質問）、画像検索など。このタスク用に新しい画像を取得しましょう。 <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-vqa.jpg" alt="Image of a couple having a picnic"/> </div> 写真提供 [Jarritos Mexican Soda](https://unsplash.com/@jarritos). 適切な指示をプロンプトすることで、モデルを画像キャプションから視覚的な質問への応答に導くことができます。 ```py >>> prompt = [ ... "Instruction: Provide an answer to the question. Use the image to answer.\n", ... "https://images.unsplash.com/photo-1623944889288-cd147dbb517c?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3540&q=80", ... "Question: Where are these people and what's the weather like? Answer:" ... ] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=20, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) Instruction: Provide an answer to the question. Use the image to answer. Question: Where are these people and what's the weather like? Answer: They're in a park in New York City, and it's a beautiful day. ``` ## Image classification IDEFICS は、次のデータを含むデータについて明示的にトレーニングしなくても、画像をさまざまなカテゴリに分類できます。これらの特定のカテゴリからのラベル付きの例。カテゴリのリストを指定し、その画像とテキストを使用して理解する機能を利用すると、モデルは画像がどのカテゴリに属する可能性が高いかを推測できます。たとえば、次のような野菜スタンドの画像があるとします。 <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-classification.jpg" alt="Image of a vegetable stand"/> </div> 写真提供：[Peter Wendt](https://unsplash.com/@peterwendt)。画像を次のいずれかのカテゴリに分類するようにモデルに指示できます。 ```py >>> categories = ['animals','vegetables', 'city landscape', 'cars', 'office'] >>> prompt = [f"Instruction: Classify the following image into a single category from the following list: {categories}.\n", ... "https://images.unsplash.com/photo-1471193945509-9ad0617afabf?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3540&q=80", ... "Category: " ... ] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=6, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) Instruction: Classify the following image into a single category from the following list: ['animals', 'vegetables', 'city landscape', 'cars', 'office']. Category: Vegetables ``` 上の例では、画像を 1 つのカテゴリに分類するようにモデルに指示していますが、ランク分類を行うようにモデルに指示することもできます。 ## Image-guided text generation よりクリエイティブなアプリケーションの場合は、画像ガイド付きテキスト生成を使用して、画像に基づいてテキストを生成できます。これは可能です製品、広告、シーンの説明などを作成するのに役立ちます。 IDEFICS に、赤いドアの単純な画像に基づいてストーリーを書くように促してみましょう。 <div class="flex justify-center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-story-generation.jpg" alt="Image of a red door with a pumpkin on the steps"/> </div> 写真提供：[Craig Tidball](https://unsplash.com/@devonshiremedia)。 ```py >>> prompt = ["Instruction: Use the image to write a story. \n", ... "https://images.unsplash.com/photo-1517086822157-2b0358e7684a?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=2203&q=80", ... "Story: \n"] >>> inputs = processor(prompt, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, num_beams=2, max_new_tokens=200, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> print(generated_text[0]) Instruction: Use the image to write a story. Story: Once upon a time, there was a little girl who lived in a house with a red door. She loved her red door. It was the prettiest door in the whole world. One day, the little girl was playing in her yard when she noticed a man standing on her doorstep. He was wearing a long black coat and a top hat. The little girl ran inside and told her mother about the man. Her mother said, “Don’t worry, honey. He’s just a friendly ghost.” The little girl wasn’t sure if she believed her mother, but she went outside anyway. When she got to the door, the man was gone. The next day, the little girl was playing in her yard again when she noticed the man standing on her doorstep. He was wearing a long black coat and a top hat. The little girl ran ``` IDEFICS は玄関先にあるカボチャに気づき、幽霊に関する不気味なハロウィーンの話をしたようです。 <Tip> このような長い出力の場合、テキスト生成戦略を微調整すると大きなメリットが得られます。これは役に立ちます生成される出力の品質が大幅に向上します。 [テキスト生成戦略](../generation_strategies) を確認してください。詳しく知ることができ。 </Tip> ## Running inference in batch mode これまでのすべてのセクションでは、IDEFICS を 1 つの例として説明しました。非常に似た方法で、推論を実行できます。プロンプトのリストを渡すことにより、サンプルのバッチを取得します。 ```py >>> prompts = [ ... [ "https://images.unsplash.com/photo-1543349689-9a4d426bee8e?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3501&q=80", ... "This is an image of ", ... ], ... [ "https://images.unsplash.com/photo-1623944889288-cd147dbb517c?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3540&q=80", ... "This is an image of ", ... ], ... [ "https://images.unsplash.com/photo-1471193945509-9ad0617afabf?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=3540&q=80", ... "This is an image of ", ... ], ... ] >>> inputs = processor(prompts, return_tensors="pt").to("cuda") >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, max_new_tokens=10, bad_words_ids=bad_words_ids) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> for i,t in enumerate(generated_text): ... print(f"{i}:\n{t}\n") 0: This is an image of the Eiffel Tower in Paris, France. 1: This is an image of a couple on a picnic blanket. 2: This is an image of a vegetable stand. ``` ## IDEFICS instruct for conversational use 会話型のユースケースの場合は、🤗 ハブでモデルの微調整された指示されたバージョンを見つけることができます。 `HuggingFaceM4/idefics-80b-instruct` および `HuggingFaceM4/idefics-9b-instruct`。これらのチェックポイントは、教師ありモデルと命令モデルを組み合わせたそれぞれの基本モデルを微調整した結果です。データセットを微調整することで、ダウンストリームのパフォーマンスを向上させながら、会話設定でモデルをより使いやすくします。会話での使用とプロンプトは、基本モデルの使用と非常に似ています。 ```py >>> import torch >>> from transformers import IdeficsForVisionText2Text, AutoProcessor >>> device = "cuda" if torch.cuda.is_available() else "cpu" >>> checkpoint = "HuggingFaceM4/idefics-9b-instruct" >>> model = IdeficsForVisionText2Text.from_pretrained(checkpoint, torch_dtype=torch.bfloat16).to(device) >>> processor = AutoProcessor.from_pretrained(checkpoint) >>> prompts = [ ... [ ... "User: What is in this image?", ... "https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG", ... "<end_of_utterance>", ... "\nAssistant: This picture depicts Idefix, the dog of Obelix in Asterix and Obelix. Idefix is running on the ground.<end_of_utterance>", ... "\nUser:", ... "https://static.wikia.nocookie.net/asterix/images/2/25/R22b.gif/revision/latest?cb=20110815073052", ... "And who is that?<end_of_utterance>", ... "\nAssistant:", ... ], ... ] >>> # --batched mode >>> inputs = processor(prompts, add_end_of_utterance_token=False, return_tensors="pt").to(device) >>> # --single sample mode >>> # inputs = processor(prompts[0], return_tensors="pt").to(device) >>> # Generation args >>> exit_condition = processor.tokenizer("<end_of_utterance>", add_special_tokens=False).input_ids >>> bad_words_ids = processor.tokenizer(["<image>", "<fake_token_around_image>"], add_special_tokens=False).input_ids >>> generated_ids = model.generate(**inputs, eos_token_id=exit_condition, bad_words_ids=bad_words_ids, max_length=100) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) >>> for i, t in enumerate(generated_text): ... print(f"{i}:\n{t}\n") ```

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{ "file_path": "transformers/docs/source/ja/tasks/idefics.md", "repo_id": "transformers", "token_count": 9887 }

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- sections: - local: index title: 🤗 Transformers - local: quicktour title: 둘러보기 - local: installation title: 설치방법 title: 시작하기 - sections: - local: pipeline_tutorial title: Pipeline으로 추론하기 - local: autoclass_tutorial title: AutoClass로 사전 학습된 인스턴스 로드하기 - local: preprocessing title: 데이터 전처리하기 - local: training title: 사전 학습된 모델 미세 조정하기 - local: run_scripts title: 스크립트로 학습하기 - local: accelerate title: 🤗 Accelerate로 분산 학습 구성하기 - local: peft title: 🤗 PEFT로 어댑터 로드 및 학습하기 - local: model_sharing title: 만든 모델 공유하기 - local: transformers_agents title: 에이전트 - local: llm_tutorial title: 대규모 언어 모델로 생성하기 title: 튜토리얼 - sections: - sections: - local: tasks/sequence_classification title: 텍스트 분류 - local: tasks/token_classification title: 토큰 분류 - local: tasks/question_answering title: 질의 응답(Question Answering) - local: tasks/language_modeling title: 인과적 언어 모델링(Causal language modeling) - local: tasks/masked_language_modeling title: 마스킹된 언어 모델링(Masked language modeling) - local: tasks/translation title: 번역 - local: tasks/summarization title: 요약 - local: tasks/multiple_choice title: 객관식 문제(Multiple Choice) title: 자연어처리 isExpanded: false - sections: - local: tasks/audio_classification title: 오디오 분류 - local: tasks/asr title: 자동 음성 인식 title: 오디오 isExpanded: false - sections: - local: tasks/image_classification title: 이미지 분류 - local: tasks/semantic_segmentation title: 의미적 분할(Semantic segmentation) - local: tasks/video_classification title: 영상 분류 - local: tasks/object_detection title: 객체 탐지 - local: tasks/zero_shot_object_detection title: 제로샷(zero-shot) 객체 탐지 - local: tasks/zero_shot_image_classification title: 제로샷(zero-shot) 이미지 분류 - local: tasks/monocular_depth_estimation title: 단일 영상 기반 깊이 추정 title: 컴퓨터 비전 isExpanded: false - sections: - local: tasks/image_captioning title: 이미지 캡셔닝 - local: tasks/document_question_answering title: 문서 질의 응답(Document Question Answering) - local: tasks/visual_question_answering title: 시각적 질의응답 (Visual Question Answering) title: 멀티모달 isExpanded: false title: 태스크 가이드 - sections: - local: fast_tokenizers title: 🤗 Tokenizers 라이브러리에서 토크나이저 사용하기 - local: multilingual title: 다국어 모델 추론하기 - local: generation_strategies title: 텍스트 생성 전략 사용자 정의 - local: create_a_model title: 모델별 API 사용하기 - local: custom_models title: 사용자 정의 모델 공유하기 - local: sagemaker title: Amazon SageMaker에서 학습 실행하기 - local: serialization title: ONNX로 내보내기 - local: tflite title: TFLite로 내보내기 - local: torchscript title: TorchScript로 내보내기 - local: in_translation title: (번역중) Benchmarks - local: in_translation title: (번역중) Notebooks with examples - local: community title: 커뮤니티 리소스 - local: custom_tools title: 사용자 정의 도구와 프롬프트 - local: troubleshooting title: 문제 해결 title: (번역중) 개발자 가이드 - sections: - local: performance title: 성능 및 확장성 - local: in_translation title: (번역중) Training on one GPU - local: perf_train_gpu_many title: 다중 GPU에서 훈련 진행하기 - local: perf_train_cpu title: CPU에서 훈련 - local: perf_train_cpu_many title: 다중 CPU에서 훈련하기 - local: in_translation title: (번역중) Training on TPUs - local: perf_train_tpu_tf title: TensorFlow로 TPU에서 훈련하기 - local: in_translation title: (번역중) Training on Specialized Hardware - local: perf_infer_cpu title: CPU로 추론하기 - local: perf_infer_gpu_one title: 하나의 GPU를 활용한 추론 - local: perf_infer_gpu_many title: 다중 GPU에서 추론 - local: in_translation title: (번역중) Inference on Specialized Hardware - local: perf_hardware title: 훈련용 사용자 맞춤형 하드웨어 - local: big_models title: 대형 모델을 인스턴스화 - local: debugging title: 디버깅 - local: hpo_train title: Trainer API를 사용한 하이퍼파라미터 탐색 - local: tf_xla title: TensorFlow 모델을 위한 XLA 통합 title: (번역중) 성능 및 확장성 - sections: - local: contributing title: 🤗 Transformers에 기여하는 방법 - local: add_new_model title: 🤗 Transformers에 새로운 모델을 추가하는 방법 - local: add_tensorflow_model title: 어떻게 🤗 Transformers 모델을 TensorFlow로 변환하나요? - local: add_new_pipeline title: 어떻게 🤗 Transformers에 파이프라인을 추가하나요? - local: testing title: 테스트 - local: pr_checks title: Pull Request에 대한 검사 title: (번역중) 기여하기 - sections: - local: philosophy title: 이념과 목표 - local: in_translation title: (번역중) Glossary - local: task_summary title: 🤗 Transformers로 할 수 있는 작업 - local: tasks_explained title: 🤗 Transformers로 작업을 해결하는 방법 - local: model_summary title: Transformer 모델군 - local: tokenizer_summary title: 토크나이저 요약 - local: attention title: 어텐션 매커니즘 - local: pad_truncation title: 패딩과 잘라내기 - local: bertology title: BERTology - local: perplexity title: 고정 길이 모델의 펄플렉서티(Perplexity) - local: pipeline_webserver title: 추론 웹 서버를 위한 파이프라인 - local: model_memory_anatomy title: 모델 학습 해부하기 title: (번역중) 개념 가이드 - sections: - sections: - local: in_translation title: (번역중) Auto Classes - local: in_translation title: (번역중) Callbacks - local: in_translation title: (번역중) Configuration - local: in_translation title: (번역중) Data Collator - local: in_translation title: (번역중) Keras callbacks - local: in_translation title: (번역중) Logging - local: in_translation title: (번역중) Models - local: in_translation title: (번역중) Text Generation - local: in_translation title: (번역중) ONNX - local: in_translation title: (번역중) Optimization - local: in_translation title: (번역중) Model outputs - local: in_translation title: (번역중) Pipelines - local: in_translation title: (번역중) Processors - local: in_translation title: (번역중) Quantization - local: in_translation title: (번역중) Tokenizer - local: in_translation title: (번역중) Trainer - local: in_translation title: (번역중) DeepSpeed Integration - local: in_translation title: (번역중) Feature Extractor - local: in_translation title: (번역중) Image Processor title: (번역중) 메인 클래스 - sections: - isExpanded: false sections: - local: in_translation title: (번역중) ALBERT - local: in_translation title: (번역중) BART - local: in_translation title: (번역중) BARThez - local: in_translation title: (번역중) BARTpho - local: in_translation title: (번역중) BERT - local: in_translation title: (번역중) BertGeneration - local: in_translation title: (번역중) BertJapanese - local: in_translation title: (번역중) Bertweet - local: in_translation title: (번역중) BigBird - local: in_translation title: (번역중) BigBirdPegasus - local: in_translation title: (번역중) BioGpt - local: in_translation title: (번역중) Blenderbot - local: in_translation title: (번역중) Blenderbot Small - local: in_translation title: (번역중) BLOOM - local: in_translation title: (번역중) BORT - local: in_translation title: (번역중) ByT5 - local: in_translation title: (번역중) CamemBERT - local: in_translation title: (번역중) CANINE - local: in_translation title: (번역중) CodeGen - local: in_translation title: (번역중) ConvBERT - local: in_translation title: (번역중) CPM - local: in_translation title: (번역중) CPMANT - local: in_translation title: (번역중) CTRL - local: in_translation title: (번역중) DeBERTa - local: in_translation title: (번역중) DeBERTa-v2 - local: in_translation title: (번역중) DialoGPT - local: in_translation title: (번역중) DistilBERT - local: in_translation title: (번역중) DPR - local: in_translation title: (번역중) ELECTRA - local: in_translation title: (번역중) Encoder Decoder Models - local: in_translation title: (번역중) ERNIE - local: in_translation title: (번역중) ErnieM - local: in_translation title: (번역중) ESM - local: in_translation title: (번역중) FLAN-T5 - local: in_translation title: (번역중) FLAN-UL2 - local: in_translation title: (번역중) FlauBERT - local: in_translation title: (번역중) FNet - local: in_translation title: (번역중) FSMT - local: in_translation title: (번역중) Funnel Transformer - local: in_translation title: (번역중) GPT - local: in_translation title: (번역중) GPT Neo - local: in_translation title: (번역중) GPT NeoX - local: in_translation title: (번역중) GPT NeoX Japanese - local: in_translation title: (번역중) GPT-J - local: in_translation title: (번역중) GPT2 - local: in_translation title: (번역중) GPTBigCode - local: in_translation title: (번역중) GPTSAN Japanese - local: in_translation title: (번역중) GPTSw3 - local: in_translation title: (번역중) HerBERT - local: in_translation title: (번역중) I-BERT - local: in_translation title: (번역중) Jukebox - local: in_translation title: (번역중) LED - local: model_doc/llama title: LLaMA - local: model_doc/llama2 title: LLaMA2 - local: in_translation title: (번역중) Longformer - local: in_translation title: (번역중) LongT5 - local: in_translation title: (번역중) LUKE - local: in_translation title: (번역중) M2M100 - local: in_translation title: (번역중) MarianMT - local: in_translation title: (번역중) MarkupLM - local: in_translation title: (번역중) MBart and MBart-50 - local: in_translation title: (번역중) MEGA - local: in_translation title: (번역중) MegatronBERT - local: in_translation title: (번역중) MegatronGPT2 - local: in_translation title: (번역중) mLUKE - local: in_translation title: (번역중) MobileBERT - local: in_translation title: (번역중) MPNet - local: in_translation title: (번역중) MT5 - local: in_translation title: (번역중) MVP - local: in_translation title: (번역중) NEZHA - local: in_translation title: (번역중) NLLB - local: in_translation title: (번역중) NLLB-MoE - local: in_translation title: (번역중) Nyströmformer - local: in_translation title: (번역중) Open-Llama - local: in_translation title: (번역중) OPT - local: in_translation title: (번역중) Pegasus - local: in_translation title: (번역중) PEGASUS-X - local: in_translation title: (번역중) PhoBERT - local: in_translation title: (번역중) PLBart - local: in_translation title: (번역중) ProphetNet - local: in_translation title: (번역중) QDQBert - local: in_translation title: (번역중) RAG - local: in_translation title: (번역중) REALM - local: in_translation title: (번역중) Reformer - local: in_translation title: (번역중) RemBERT - local: in_translation title: (번역중) RetriBERT - local: in_translation title: (번역중) RoBERTa - local: in_translation title: (번역중) RoBERTa-PreLayerNorm - local: in_translation title: (번역중) RoCBert - local: in_translation title: (번역중) RoFormer - local: in_translation title: (번역중) Splinter - local: in_translation title: (번역중) SqueezeBERT - local: in_translation title: (번역중) SwitchTransformers - local: in_translation title: (번역중) T5 - local: in_translation title: (번역중) T5v1.1 - local: in_translation title: (번역중) TAPEX - local: in_translation title: (번역중) Transformer XL - local: in_translation title: (번역중) UL2 - local: in_translation title: (번역중) X-MOD - local: in_translation title: (번역중) XGLM - local: in_translation title: (번역중) XLM - local: in_translation title: (번역중) XLM-ProphetNet - local: in_translation title: (번역중) XLM-RoBERTa - local: in_translation title: (번역중) XLM-RoBERTa-XL - local: in_translation title: (번역중) XLM-V - local: in_translation title: (번역중) XLNet - local: in_translation title: (번역중) YOSO title: (번역중) 텍스트 모델 - isExpanded: false sections: - local: in_translation title: (번역중) BEiT - local: in_translation title: (번역중) BiT - local: in_translation title: (번역중) Conditional DETR - local: in_translation title: (번역중) ConvNeXT - local: in_translation title: (번역중) ConvNeXTV2 - local: in_translation title: (번역중) CvT - local: in_translation title: (번역중) Deformable DETR - local: in_translation title: (번역중) DeiT - local: in_translation title: (번역중) DETA - local: in_translation title: (번역중) DETR - local: in_translation title: (번역중) DiNAT - local: in_translation title: (번역중) DiT - local: in_translation title: (번역중) DPT - local: in_translation title: (번역중) EfficientFormer - local: in_translation title: (번역중) EfficientNet - local: in_translation title: (번역중) FocalNet - local: in_translation title: (번역중) GLPN - local: in_translation title: (번역중) ImageGPT - local: in_translation title: (번역중) LeViT - local: in_translation title: (번역중) Mask2Former - local: in_translation title: (번역중) MaskFormer - local: in_translation title: (번역중) MobileNetV1 - local: in_translation title: (번역중) MobileNetV2 - local: in_translation title: (번역중) MobileViT - local: in_translation title: (번역중) NAT - local: in_translation title: (번역중) PoolFormer - local: in_translation title: (번역중) RegNet - local: in_translation title: (번역중) ResNet - local: in_translation title: (번역중) SegFormer - local: in_translation title: (번역중) Swin Transformer - local: in_translation title: (번역중) Swin Transformer V2 - local: in_translation title: (번역중) Swin2SR - local: in_translation title: (번역중) Table Transformer - local: in_translation title: (번역중) TimeSformer - local: in_translation title: (번역중) UperNet - local: in_translation title: (번역중) VAN - local: in_translation title: (번역중) VideoMAE - local: in_translation title: (번역중) Vision Transformer (ViT) - local: in_translation title: (번역중) ViT Hybrid - local: in_translation title: (번역중) ViTMAE - local: in_translation title: (번역중) ViTMSN - local: in_translation title: (번역중) YOLOS title: (번역중) 비전 모델 - isExpanded: false sections: - local: in_translation title: (번역중) Audio Spectrogram Transformer - local: in_translation title: (번역중) CLAP - local: in_translation title: (번역중) Hubert - local: in_translation title: (번역중) MCTCT - local: in_translation title: (번역중) SEW - local: in_translation title: (번역중) SEW-D - local: in_translation title: (번역중) Speech2Text - local: in_translation title: (번역중) Speech2Text2 - local: in_translation title: (번역중) SpeechT5 - local: in_translation title: (번역중) UniSpeech - local: in_translation title: (번역중) UniSpeech-SAT - local: in_translation title: (번역중) Wav2Vec2 - local: in_translation title: (번역중) Wav2Vec2-Conformer - local: in_translation title: (번역중) Wav2Vec2Phoneme - local: in_translation title: (번역중) WavLM - local: model_doc/whisper title: Whisper - local: in_translation title: (번역중) XLS-R - local: in_translation title: (번역중) XLSR-Wav2Vec2 title: (번역중) 오디오 모델 - isExpanded: false sections: - local: in_translation title: (번역중) ALIGN - local: in_translation title: (번역중) AltCLIP - local: in_translation title: (번역중) BLIP - local: in_translation title: (번역중) BLIP-2 - local: in_translation title: (번역중) BridgeTower - local: in_translation title: (번역중) Chinese-CLIP - local: in_translation title: (번역중) CLIP - local: in_translation title: (번역중) CLIPSeg - local: in_translation title: (번역중) Data2Vec - local: in_translation title: (번역중) DePlot - local: in_translation title: (번역중) Donut - local: in_translation title: (번역중) FLAVA - local: in_translation title: (번역중) GIT - local: in_translation title: (번역중) GroupViT - local: in_translation title: (번역중) LayoutLM - local: in_translation title: (번역중) LayoutLMV2 - local: in_translation title: (번역중) LayoutLMV3 - local: in_translation title: (번역중) LayoutXLM - local: in_translation title: (번역중) LiLT - local: in_translation title: (번역중) LXMERT - local: in_translation title: (번역중) MatCha - local: in_translation title: (번역중) MGP-STR - local: in_translation title: (번역중) OneFormer - local: in_translation title: (번역중) OWL-ViT - local: in_translation title: (번역중) Perceiver - local: in_translation title: (번역중) Pix2Struct - local: in_translation title: (번역중) Segment Anything - local: in_translation title: (번역중) Speech Encoder Decoder Models - local: in_translation title: (번역중) TAPAS - local: in_translation title: (번역중) TrOCR - local: in_translation title: (번역중) TVLT - local: in_translation title: (번역중) ViLT - local: in_translation title: (번역중) Vision Encoder Decoder Models - local: in_translation title: (번역중) Vision Text Dual Encoder - local: in_translation title: (번역중) VisualBERT - local: in_translation title: (번역중) X-CLIP title: (번역중) 멀티모달 모델 - isExpanded: false sections: - local: in_translation title: (번역중) Decision Transformer - local: in_translation title: (번역중) Trajectory Transformer title: (번역중) 강화학습 모델 - isExpanded: false sections: - local: in_translation title: (번역중) Informer - local: in_translation title: (번역중) Time Series Transformer title: (번역중) 시계열 모델 - isExpanded: false sections: - local: in_translation title: (번역중) Graphormer title: (번역중) Graph models title: (번역중) 모델 - sections: - local: in_translation title: (번역중) Custom Layers and Utilities - local: in_translation title: (번역중) Utilities for pipelines - local: in_translation title: (번역중) Utilities for Tokenizers - local: in_translation title: (번역중) Utilities for Trainer - local: in_translation title: (번역중) Utilities for Generation - local: in_translation title: (번역중) Utilities for Image Processors - local: in_translation title: (번역중) Utilities for Audio processing - local: in_translation title: (번역중) General Utilities - local: in_translation title: (번역중) Utilities for Time Series title: (번역중) Internal Helpers title: (번역중) API

transformers/docs/source/ko/_toctree.yml/0

{ "file_path": "transformers/docs/source/ko/_toctree.yml", "repo_id": "transformers", "token_count": 12639 }

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# Text generation strategies[[text-generation-strategies]] 텍스트 생성은 개방형 텍스트 작성, 요약, 번역 등 다양한 자연어 처리(NLP) 작업에 필수적입니다. 이는 또한 음성-텍스트 변환, 시각-텍스트 변환과 같이 텍스트를 출력으로 하는 여러 혼합 모달리티 응용 프로그램에서도 중요한 역할을 합니다. 텍스트 생성을 가능하게 하는 몇몇 모델로는 GPT2, XLNet, OpenAI GPT, CTRL, TransformerXL, XLM, Bart, T5, GIT, Whisper 등이 있습니다. [`~transformers.generation_utils.GenerationMixin.generate`] 메서드를 활용하여 다음과 같은 다양한 작업들에 대해 텍스트 결과물을 생성하는 몇 가지 예시를 살펴보세요: * [텍스트 요약](./tasks/summarization#inference) * [이미지 캡셔닝](./model_doc/git#transformers.GitForCausalLM.forward.example) * [오디오 전사](./model_doc/whisper#transformers.WhisperForConditionalGeneration.forward.example) generate 메소드에 입력되는 값들은 모델의 데이터 형태에 따라 달라집니다. 이 값들은 AutoTokenizer나 AutoProcessor와 같은 모델의 전처리 클래스에 의해 반환됩니다. 모델의 전처리 장치가 하나 이상의 입력 유형을 생성하는 경우, 모든 입력을 generate()에 전달해야 합니다. 각 모델의 전처리 장치에 대해서는 해당 모델의 문서에서 자세히 알아볼 수 있습니다. 텍스트를 생성하기 위해 출력 토큰을 선택하는 과정을 디코딩이라고 하며, `generate()` 메소드가 사용할 디코딩 전략을 사용자가 커스터마이징할 수 있습니다. 디코딩 전략을 수정하는 것은 훈련 가능한 매개변수의 값들을 변경하지 않지만, 생성된 출력의 품질에 눈에 띄는 영향을 줄 수 있습니다. 이는 텍스트에서 반복을 줄이고, 더 일관성 있게 만드는 데 도움을 줄 수 있습니다. 이 가이드에서는 다음과 같은 내용을 다룹니다: * 기본 생성 설정 * 일반적인 디코딩 전략과 주요 파라미터 * 🤗 Hub에서 미세 조정된 모델과 함께 사용자 정의 생성 설정을 저장하고 공유하는 방법 ## 기본 텍스트 생성 설정[[default-text-generation-configuration]] 모델의 디코딩 전략은 생성 설정에서 정의됩니다. 사전 훈련된 모델을 [`pipeline`] 내에서 추론에 사용할 때, 모델은 내부적으로 기본 생성 설정을 적용하는 `PreTrainedModel.generate()` 메소드를 호출합니다. 사용자가 모델과 함께 사용자 정의 설정을 저장하지 않았을 경우에도 기본 설정이 사용됩니다. 모델을 명시적으로 로드할 때, `model.generation_config`을 통해 제공되는 생성 설정을 검사할 수 있습니다. ```python >>> from transformers import AutoModelForCausalLM >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2") >>> model.generation_config GenerationConfig { "bos_token_id": 50256, "eos_token_id": 50256, } ``` `model.generation_config`를 출력하면 기본 설정과 다른 값들만 표시되고, 기본값들은 나열되지 않습니다. 기본 생성 설정은 입력 프롬프트와 출력을 합친 최대 크기를 20 토큰으로 제한하여 리소스 부족을 방지합니다. 기본 디코딩 전략은 탐욕 탐색(greedy search)으로, 다음 토큰으로 가장 높은 확률을 가진 토큰을 선택하는 가장 단순한 디코딩 전략입니다. 많은 작업과 작은 출력 크기에 대해서는 이 방법이 잘 작동하지만, 더 긴 출력을 생성할 때 사용하면 매우 반복적인 결과를 생성하게 될 수 있습니다. ## 텍스트 생성 사용자 정의[[customize-text-generation]] 파라미터와 해당 값을 [`generate`] 메소드에 직접 전달하여 `generation_config`을 재정의할 수 있습니다: ```python >>> my_model.generate(**inputs, num_beams=4, do_sample=True) # doctest: +SKIP ``` 기본 디코딩 전략이 대부분의 작업에 잘 작동한다 하더라도, 조정할 수 있는 몇 가지 파라미터가 있습니다. 일반적으로 조정되는 파라미터에는 다음과 같은 것들이 포함됩니다: - `max_new_tokens`: 생성할 최대 토큰 수입니다. 즉, 프롬프트에 있는 토큰을 제외한 출력 시퀀스의 크기입니다. 출력의 길이를 중단 기준으로 사용하는 대신, 전체 생성물이 일정 시간을 초과할 때 생성을 중단하기로 선택할 수도 있습니다. 더 알아보려면 [`StoppingCriteria`]를 확인하세요. - `num_beams`: 1보다 큰 수의 빔을 지정함으로써, 탐욕 탐색(greedy search)에서 빔 탐색(beam search)으로 전환하게 됩니다. 이 전략은 각 시간 단계에서 여러 가설을 평가하고 결국 전체 시퀀스에 대해 가장 높은 확률을 가진 가설을 선택합니다. 이는 초기 토큰의 확률이 낮아 탐욕 탐색에 의해 무시되었을 높은 확률의 시퀀스를 식별할 수 있는 장점을 가집니다. - `do_sample`: 이 매개변수를 `True`로 설정하면, 다항 샘플링, 빔 탐색 다항 샘플링, Top-K 샘플링 및 Top-p 샘플링과 같은 디코딩 전략을 활성화합니다. 이러한 전략들은 전체 어휘에 대한 확률 분포에서 다음 토큰을 선택하며, 전략별로 특정 조정이 적용됩니다. - `num_return_sequences`: 각 입력에 대해 반환할 시퀀스 후보의 수입니다. 이 옵션은 빔 탐색(beam search)의 변형과 샘플링과 같이 여러 시퀀스 후보를 지원하는 디코딩 전략에만 사용할 수 있습니다. 탐욕 탐색(greedy search)과 대조 탐색(contrastive search) 같은 디코딩 전략은 단일 출력 시퀀스를 반환합니다. ## 모델에 사용자 정의 디코딩 전략 저장[[save-a-custom-decoding-strategy-with-your-model]] 특정 생성 설정을 가진 미세 조정된 모델을 공유하고자 할 때, 다음 단계를 따를 수 있습니다: * [`GenerationConfig`] 클래스 인스턴스를 생성합니다. * 디코딩 전략 파라미터를 설정합니다. * 생성 설정을 [`GenerationConfig.save_pretrained`]를 사용하여 저장하며, `config_file_name` 인자는 비워둡니다. * 모델의 저장소에 설정을 업로드하기 위해 `push_to_hub`를 `True`로 설정합니다. ```python >>> from transformers import AutoModelForCausalLM, GenerationConfig >>> model = AutoModelForCausalLM.from_pretrained("my_account/my_model") # doctest: +SKIP >>> generation_config = GenerationConfig( ... max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id ... ) >>> generation_config.save_pretrained("my_account/my_model", push_to_hub=True) # doctest: +SKIP ``` 단일 디렉토리에 여러 생성 설정을 저장할 수 있으며, 이때 [`GenerationConfig.save_pretrained`]의 `config_file_name` 인자를 사용합니다. 나중에 [`GenerationConfig.from_pretrained`]로 이들을 인스턴스화할 수 있습니다. 이는 단일 모델에 대해 여러 생성 설정을 저장하고 싶을 때 유용합니다(예: 샘플링을 이용한 창의적 텍스트 생성을 위한 하나, 빔 탐색을 이용한 요약을 위한 다른 하나 등). 모델에 설정 파일을 추가하기 위해 적절한 Hub 권한을 가지고 있어야 합니다. ```python >>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig >>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small") >>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small") >>> translation_generation_config = GenerationConfig( ... num_beams=4, ... early_stopping=True, ... decoder_start_token_id=0, ... eos_token_id=model.config.eos_token_id, ... pad_token=model.config.pad_token_id, ... ) >>> # 팁: Hub에 push하려면 `push_to_hub=True`를 추가 >>> translation_generation_config.save_pretrained("/tmp", "translation_generation_config.json") >>> # 명명된 생성 설정 파일을 사용하여 생성을 매개변수화할 수 있습니다. >>> generation_config = GenerationConfig.from_pretrained("/tmp", "translation_generation_config.json") >>> inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt") >>> outputs = model.generate(**inputs, generation_config=generation_config) >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ['Les fichiers de configuration sont faciles à utiliser!'] ``` ## 스트리밍[[streaming]] `generate()` 메소드는 `streamer` 입력을 통해 스트리밍을 지원합니다. `streamer` 입력은 `put()`과 `end()` 메소드를 가진 클래스의 인스턴스와 호환됩니다. 내부적으로, `put()`은 새 토큰을 추가하는 데 사용되며, `end()`는 텍스트 생성의 끝을 표시하는 데 사용됩니다. <Tip warning={true}> 스트리머 클래스의 API는 아직 개발 중이며, 향후 변경될 수 있습니다. </Tip> 실제로 다양한 목적을 위해 자체 스트리밍 클래스를 만들 수 있습니다! 또한, 기본적인 스트리밍 클래스들도 준비되어 있어 바로 사용할 수 있습니다. 예를 들어, [`TextStreamer`] 클래스를 사용하여 `generate()`의 출력을 화면에 한 단어씩 스트리밍할 수 있습니다: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer >>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") >>> streamer = TextStreamer(tok) >>> # 스트리머는 평소와 같은 출력값을 반환할 뿐만 아니라 생성된 텍스트도 표준 출력(stdout)으로 출력합니다. >>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20) An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven, ``` ## 디코딩 전략[[decoding-strategies]] `generate()` 매개변수와 궁극적으로 `generation_config`의 특정 조합을 사용하여 특정 디코딩 전략을 활성화할 수 있습니다. 이 개념이 처음이라면, 흔히 사용되는 디코딩 전략이 어떻게 작동하는지 설명하는 [이 블로그 포스트](https://huggingface.co/blog/how-to-generate)를 읽어보는 것을 추천합니다. 여기서는 디코딩 전략을 제어하는 몇 가지 매개변수를 보여주고, 이를 어떻게 사용할 수 있는지 설명하겠습니다. ### 탐욕 탐색(Greedy Search)[[greedy-search]] [`generate`]는 기본적으로 탐욕 탐색 디코딩을 사용하므로 이를 활성화하기 위해 별도의 매개변수를 지정할 필요가 없습니다. 이는 `num_beams`가 1로 설정되고 `do_sample=False`로 되어 있다는 의미입니다." ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> prompt = "I look forward to" >>> checkpoint = "distilbert/distilgpt2" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> outputs = model.generate(**inputs) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n'] ``` ### 대조 탐색(Contrastive search)[[contrastive-search]] 2022년 논문 [A Contrastive Framework for Neural Text Generation](https://arxiv.org/abs/2202.06417)에서 제안된 대조 탐색 디코딩 전략은 반복되지 않으면서도 일관된 긴 출력을 생성하는 데 있어 우수한 결과를 보였습니다. 대조 탐색이 작동하는 방식을 알아보려면 [이 블로그 포스트](https://huggingface.co/blog/introducing-csearch)를 확인하세요. 대조 탐색의 동작을 가능하게 하고 제어하는 두 가지 주요 매개변수는 `penalty_alpha`와 `top_k`입니다: ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM >>> checkpoint = "openai-community/gpt2-large" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> prompt = "Hugging Face Company is" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> outputs = model.generate(**inputs, penalty_alpha=0.6, top_k=4, max_new_tokens=100) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Hugging Face Company is a family owned and operated business. We pride ourselves on being the best in the business and our customer service is second to none.\n\nIf you have any questions about our products or services, feel free to contact us at any time. We look forward to hearing from you!'] ``` ### 다항 샘플링(Multinomial sampling)[[multinomial-sampling]] 탐욕 탐색(greedy search)이 항상 가장 높은 확률을 가진 토큰을 다음 토큰으로 선택하는 것과 달리, 다항 샘플링(multinomial sampling, 조상 샘플링(ancestral sampling)이라고도 함)은 모델이 제공하는 전체 어휘에 대한 확률 분포를 기반으로 다음 토큰을 무작위로 선택합니다. 0이 아닌 확률을 가진 모든 토큰은 선택될 기회가 있으므로, 반복의 위험을 줄일 수 있습니다. 다항 샘플링을 활성화하려면 `do_sample=True` 및 `num_beams=1`을 설정하세요. ```python >>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed >>> set_seed(0) # 재현성을 위해 >>> checkpoint = "openai-community/gpt2-large" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> prompt = "Today was an amazing day because" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> outputs = model.generate(**inputs, do_sample=True, num_beams=1, max_new_tokens=100) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Today was an amazing day because when you go to the World Cup and you don\'t, or when you don\'t get invited, that\'s a terrible feeling."'] ``` ### 빔 탐색(Beam-search) 디코딩[[beam-search-decoding]] 탐욕 검색(greedy search)과 달리, 빔 탐색(beam search) 디코딩은 각 시간 단계에서 여러 가설을 유지하고 결국 전체 시퀀스에 대해 가장 높은 확률을 가진 가설을 선택합니다. 이는 낮은 확률의 초기 토큰으로 시작하고 그리디 검색에서 무시되었을 가능성이 높은 시퀀스를 식별하는 이점이 있습니다. 이 디코딩 전략을 활성화하려면 `num_beams` (추적할 가설 수라고도 함)를 1보다 크게 지정하세요. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> prompt = "It is astonishing how one can" >>> checkpoint = "openai-community/gpt2-medium" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> outputs = model.generate(**inputs, num_beams=5, max_new_tokens=50) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['It is astonishing how one can have such a profound impact on the lives of so many people in such a short period of time."\n\nHe added: "I am very proud of the work I have been able to do in the last few years.\n\n"I have'] ``` ### 빔 탐색 다항 샘플링(Beam-search multinomial sampling)[[beam-search-multinomial-sampling]] 이 디코딩 전략은 이름에서 알 수 있듯이 빔 탐색과 다항 샘플링을 결합한 것입니다. 이 디코딩 전략을 사용하기 위해서는 `num_beams`를 1보다 큰 값으로 설정하고, `do_sample=True`로 설정해야 합니다. ```python >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, set_seed >>> set_seed(0) # 재현성을 위해 >>> prompt = "translate English to German: The house is wonderful." >>> checkpoint = "google-t5/t5-small" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) >>> outputs = model.generate(**inputs, num_beams=5, do_sample=True) >>> tokenizer.decode(outputs[0], skip_special_tokens=True) 'Das Haus ist wunderbar.' ``` ### 다양한 빔 탐색 디코딩(Diverse beam search decoding)[[diverse-beam-search-decoding]] 다양한 빔 탐색(Decoding) 전략은 선택할 수 있는 더 다양한 빔 시퀀스 집합을 생성할 수 있게 해주는 빔 탐색 전략의 확장입니다. 이 방법은 어떻게 작동하는지 알아보려면, [다양한 빔 탐색: 신경 시퀀스 모델에서 다양한 솔루션 디코딩하기](https://arxiv.org/pdf/1610.02424.pdf)를 참조하세요. 이 접근 방식은 세 가지 주요 매개변수를 가지고 있습니다: `num_beams`, `num_beam_groups`, 그리고 `diversity_penalty`. 다양성 패널티는 그룹 간에 출력이 서로 다르게 하기 위한 것이며, 각 그룹 내에서 빔 탐색이 사용됩니다. ```python >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> checkpoint = "google/pegasus-xsum" >>> prompt = ( ... "The Permaculture Design Principles are a set of universal design principles " ... "that can be applied to any location, climate and culture, and they allow us to design " ... "the most efficient and sustainable human habitation and food production systems. " ... "Permaculture is a design system that encompasses a wide variety of disciplines, such " ... "as ecology, landscape design, environmental science and energy conservation, and the " ... "Permaculture design principles are drawn from these various disciplines. Each individual " ... "design principle itself embodies a complete conceptual framework based on sound " ... "scientific principles. When we bring all these separate principles together, we can " ... "create a design system that both looks at whole systems, the parts that these systems " ... "consist of, and how those parts interact with each other to create a complex, dynamic, " ... "living system. Each design principle serves as a tool that allows us to integrate all " ... "the separate parts of a design, referred to as elements, into a functional, synergistic, " ... "whole system, where the elements harmoniously interact and work together in the most " ... "efficient way possible." ... ) >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) >>> outputs = model.generate(**inputs, num_beams=5, num_beam_groups=5, max_new_tokens=30, diversity_penalty=1.0) >>> tokenizer.decode(outputs[0], skip_special_tokens=True) 'The Design Principles are a set of universal design principles that can be applied to any location, climate and culture, and they allow us to design the' ``` 이 가이드에서는 다양한 디코딩 전략을 가능하게 하는 주요 매개변수를 보여줍니다. [`generate`] 메서드에 대한 고급 매개변수가 존재하므로 [`generate`] 메서드의 동작을 더욱 세부적으로 제어할 수 있습니다. 사용 가능한 매개변수의 전체 목록은 [API 문서](./main_classes/text_generation.md)를 참조하세요. ### 추론 디코딩(Speculative Decoding)[[speculative-decoding]] 추론 디코딩(보조 디코딩(assisted decoding)으로도 알려짐)은 동일한 토크나이저를 사용하는 훨씬 작은 보조 모델을 활용하여 몇 가지 후보 토큰을 생성하는 상위 모델의 디코딩 전략을 수정한 것입니다. 주 모델은 단일 전방 통과로 후보 토큰을 검증함으로써 디코딩 과정을 가속화합니다. `do_sample=True`일 경우, [추론 디코딩 논문](https://arxiv.org/pdf/2211.17192.pdf)에 소개된 토큰 검증과 재샘플링 방식이 사용됩니다. 현재, 탐욕 검색(greedy search)과 샘플링만이 지원되는 보조 디코딩(assisted decoding) 기능을 통해, 보조 디코딩은 배치 입력을 지원하지 않습니다. 보조 디코딩에 대해 더 알고 싶다면, [이 블로그 포스트](https://huggingface.co/blog/assisted-generation)를 확인해 주세요. 보조 디코딩을 활성화하려면 모델과 함께 `assistant_model` 인수를 설정하세요. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> prompt = "Alice and Bob" >>> checkpoint = "EleutherAI/pythia-1.4b-deduped" >>> assistant_checkpoint = "EleutherAI/pythia-160m-deduped" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint) >>> outputs = model.generate(**inputs, assistant_model=assistant_model) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a'] ``` 샘플링 방법과 함께 보조 디코딩을 사용하는 경우 다항 샘플링과 마찬가지로 `temperature` 인수를 사용하여 무작위성을 제어할 수 있습니다. 그러나 보조 디코딩에서는 `temperature`를 낮추면 대기 시간을 개선하는 데 도움이 될 수 있습니다. ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed >>> set_seed(42) # 재현성을 위해 >>> prompt = "Alice and Bob" >>> checkpoint = "EleutherAI/pythia-1.4b-deduped" >>> assistant_checkpoint = "EleutherAI/pythia-160m-deduped" >>> tokenizer = AutoTokenizer.from_pretrained(checkpoint) >>> inputs = tokenizer(prompt, return_tensors="pt") >>> model = AutoModelForCausalLM.from_pretrained(checkpoint) >>> assistant_model = AutoModelForCausalLM.from_pretrained(assistant_checkpoint) >>> outputs = model.generate(**inputs, assistant_model=assistant_model, do_sample=True, temperature=0.5) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['Alice and Bob are going to the same party. It is a small party, in a small'] ```

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# CPU에서 효율적인 추론하기 [[efficient-inference-on-cpu]] 이 가이드는 CPU에서 대규모 모델을 효율적으로 추론하는 방법에 중점을 두고 있습니다. ## 더 빠른 추론을 위한 `BetterTransformer` [[bettertransformer-for-faster-inference]] 우리는 최근 CPU에서 텍스트, 이미지 및 오디오 모델의 빠른 추론을 위해 `BetterTransformer`를 통합했습니다. 이 통합에 대한 더 자세한 내용은 [이 문서](https://huggingface.co/docs/optimum/bettertransformer/overview)를 참조하세요. ## PyTorch JIT 모드 (TorchScript) [[pytorch-jitmode-torchscript]] TorchScript는 PyTorch 코드에서 직렬화와 최적화가 가능한 모델을 생성할때 쓰입니다. TorchScript로 만들어진 프로그램은 기존 Python 프로세스에서 저장한 뒤, 종속성이 없는 새로운 프로세스로 가져올 수 있습니다. PyTorch의 기본 설정인 `eager` 모드와 비교했을때, `jit` 모드는 연산자 결합과 같은 최적화 방법론을 통해 모델 추론에서 대부분 더 나은 성능을 제공합니다. TorchScript에 대한 친절한 소개는 [PyTorch TorchScript 튜토리얼](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html#tracing-modules)을 참조하세요. ### JIT 모드와 함께하는 IPEX 그래프 최적화 [[ipex-graph-optimization-with-jitmode]] Intel® Extension for PyTorch(IPEX)는 Transformers 계열 모델의 jit 모드에서 추가적인 최적화를 제공합니다. jit 모드와 더불어 Intel® Extension for PyTorch(IPEX)를 활용하시길 강력히 권장드립니다. Transformers 모델에서 자주 사용되는 일부 연산자 패턴은 이미 jit 모드 연산자 결합(operator fusion)의 형태로 Intel® Extension for PyTorch(IPEX)에서 지원되고 있습니다. Multi-head-attention, Concat Linear, Linear+Add, Linear+Gelu, Add+LayerNorm 결합 패턴 등이 이용 가능하며 활용했을 때 성능이 우수합니다. 연산자 결합의 이점은 사용자에게 고스란히 전달됩니다. 분석에 따르면, 질의 응답, 텍스트 분류 및 토큰 분류와 같은 가장 인기 있는 NLP 태스크 중 약 70%가 이러한 결합 패턴을 사용하여 Float32 정밀도와 BFloat16 혼합 정밀도 모두에서 성능상의 이점을 얻을 수 있습니다. [IPEX 그래프 최적화](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html)에 대한 자세한 정보를 확인하세요. #### IPEX 설치: [[ipex-installation]] IPEX 배포 주기는 PyTorch를 따라서 이루어집니다. 자세한 정보는 [IPEX 설치 방법](https://intel.github.io/intel-extension-for-pytorch/)을 확인하세요. ### JIT 모드 사용법 [[usage-of-jitmode]] 평가 또는 예측을 위해 Trainer에서 JIT 모드를 사용하려면 Trainer의 명령 인수에 `jit_mode_eval`을 추가해야 합니다. <Tip warning={true}> PyTorch의 버전이 1.14.0 이상이라면, jit 모드는 jit.trace에서 dict 입력이 지원되므로, 모든 모델의 예측과 평가가 개선될 수 있습니다. PyTorch의 버전이 1.14.0 미만이라면, 질의 응답 모델과 같이 forward 매개변수의 순서가 jit.trace의 튜플 입력 순서와 일치하는 모델에 득이 될 수 있습니다. 텍스트 분류 모델과 같이 forward 매개변수 순서가 jit.trace의 튜플 입력 순서와 다른 경우, jit.trace가 실패하며 예외가 발생합니다. 이때 예외상황을 사용자에게 알리기 위해 Logging이 사용됩니다. </Tip> [Transformers 질의 응답](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering)의 사용 사례 예시를 참조하세요. - CPU에서 jit 모드를 사용한 추론: <pre>python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ <b>--jit_mode_eval </b></pre> - CPU에서 IPEX와 함께 jit 모드를 사용한 추론: <pre>python run_qa.py \ --model_name_or_path csarron/bert-base-uncased-squad-v1 \ --dataset_name squad \ --do_eval \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir /tmp/ \ --no_cuda \ <b>--use_ipex \</b> <b>--jit_mode_eval</b></pre>

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- sections: - local: index title: 🤗 Transformers - local: quicktour title: Lawatan cepat - local: installation title: Pemasangan title: Mulakan - sections: - local: pipeline_tutorial title: Jalankan inferens dengan saluran paip - local: autoclass_tutorial title: Tulis kod mudah alih dengan AutoClass - local: preprocessing title: Praproses data - local: training title: Perhalusi model yang telah dilatih - local: run_scripts title: Latih dengan skrip - local: accelerate title: Sediakan latihan yang diedarkan dengan 🤗 Accelerate - local: model_sharing title: Kongsi model anda - local: transformers_agents title: Ejen title: Tutorials - sections: - sections: - local: tasks/sequence_classification title: Klasifikasi teks - local: tasks/token_classification title: Klasifikasi token - local: tasks/question_answering title: Soalan menjawab - local: tasks/language_modeling title: Pemodelan bahasa sebab-akibat - local: tasks/masked_language_modeling title: Pemodelan bahasa Masked - local: tasks/translation title: Terjemahan - local: tasks/summarization title: Rumusan - local: tasks/multiple_choice title: Pilihan title: Natural Language Processing isExpanded: false - sections: - local: tasks/audio_classification title: Klasifikasi audio - local: tasks/asr title: Pengecaman pertuturan automatik title: Audio isExpanded: false - sections: - local: tasks/image_classification title: Klasifikasi imej - local: tasks/semantic_segmentation title: Segmentasi semantik - local: tasks/video_classification title: Klasifikasi video - local: tasks/object_detection title: Pengesanan objek - local: tasks/zero_shot_object_detection title: Pengesanan objek Zero-Shot - local: tasks/zero_shot_image_classification title: Klasifikasi imej tangkapan Zero-Shot - local: tasks/monocular_depth_estimation title: Anggaran kedalaman title: Visi komputer isExpanded: false - sections: - local: tasks/image_captioning title: Kapsyen imej - local: tasks/document_question_answering title: Menjawab Soalan Dokumen - local: tasks/text-to-speech title: Teks kepada ucapan title: Multimodal isExpanded: false title: Panduan Tugasan - sections: - local: fast_tokenizers title: Gunakan tokenizer cepat dari 🤗 Tokenizers - local: multilingual title: Jalankan inferens dengan model berbilang bahasa - local: generation_strategies title: Sesuaikan strategi penjanaan teks - local: create_a_model title: Gunakan API khusus model - local: custom_models title: Kongsi model tersuai - local: sagemaker title: Jalankan latihan di Amazon SageMaker - local: serialization title: Eksport ke ONNX - local: torchscript title: Eksport ke TorchScript - local: benchmarks title: Penanda aras - local: Buku nota dengan contoh title: Notebooks with examples - local: Sumber komuniti title: Community resources - local: Sumber komuniti title: Custom Tools and Prompts - local: Alat dan Gesaan Tersuai title: Selesaikan masalah title: Panduan Developer - sections: - local: performance title: Gambaran keseluruhan - local: perf_train_gpu_one title: Latihan pada satu GPU - local: perf_train_gpu_many title: Latihan pada banyak GPU - local: perf_train_cpu title: Latihan mengenai CPU - local: perf_train_cpu_many title: Latihan pada banyak CPU - local: perf_train_tpu title: Latihan mengenai TPU - local: perf_train_tpu_tf title: Latihan tentang TPU dengan TensorFlow - local: perf_train_special title: Latihan mengenai Perkakasan Khusus - local: perf_infer_cpu title: Inferens pada CPU - local: perf_infer_gpu_one title: Inferens pada satu GPU - local: perf_infer_gpu_many title: Inferens pada banyak GPUs - local: perf_infer_special title: Inferens pada Perkakasan Khusus - local: perf_hardware title: Perkakasan tersuai untuk latihan - local: big_models title: Menghidupkan model besar - local: debugging title: Penyahpepijatan - local: hpo_train title: Carian Hiperparameter menggunakan API Pelatih - local: tf_xla title: Penyepaduan XLA untuk Model TensorFlow title: Prestasi dan kebolehskalaan - sections: - local: contributing title: Bagaimana untuk menyumbang kepada transformer? - local: add_new_model title: Bagaimana untuk menambah model pada 🤗 Transformers? - local: add_tensorflow_model title: Bagaimana untuk menukar model Transformers kepada TensorFlow? - local: add_new_pipeline title: Bagaimana untuk menambah saluran paip ke 🤗 Transformers? - local: testing title: Ujian - local: pr_checks title: Menyemak Permintaan Tarik title: Sumbangkan - sections: - local: philosophy title: Falsafah - local: glossary title: Glosari - local: task_summary title: Apa 🤗 Transformers boleh buat - local: tasks_explained title: Bagaimana 🤗 Transformers menyelesaikan tugasan - local: model_summary title: Keluarga model Transformer - local: tokenizer_summary title: Ringkasan tokenizer - local: attention title: Mekanisme perhatian - local: pad_truncation title: Padding dan pemotongan - local: bertology title: BERTology - local: perplexity title: Kekeliruan model panjang tetap - local: pipeline_webserver title: Saluran paip untuk inferens pelayan web title: Panduan konsep - sections: - sections: - local: main_classes/agent title: Ejen dan Alat - local: model_doc/auto title: Kelas Auto - local: main_classes/callback title: Panggilan balik - local: main_classes/configuration title: Configuration - local: main_classes/data_collator title: Data Collator - local: main_classes/keras_callbacks title: Keras callbacks - local: main_classes/logging title: Logging - local: main_classes/model title: Models - local: main_classes/text_generation title: Text Generation - local: main_classes/onnx title: ONNX - local: main_classes/optimizer_schedules title: Optimization - local: main_classes/output title: Model outputs - local: main_classes/pipelines title: Pipelines - local: main_classes/processors title: Processors - local: main_classes/quantization title: Quantization - local: main_classes/tokenizer title: Tokenizer - local: main_classes/trainer title: Trainer - local: main_classes/deepspeed title: DeepSpeed Integration - local: main_classes/feature_extractor title: Feature Extractor - local: main_classes/image_processor title: Image Processor title: Main Classes - sections: - isExpanded: false sections: - local: model_doc/albert title: ALBERT - local: model_doc/bart title: BART - local: model_doc/barthez title: BARThez - local: model_doc/bartpho title: BARTpho - local: model_doc/bert title: BERT - local: model_doc/bert-generation title: BertGeneration - local: model_doc/bert-japanese title: BertJapanese - local: model_doc/bertweet title: Bertweet - local: model_doc/big_bird title: BigBird - local: model_doc/bigbird_pegasus title: BigBirdPegasus - local: model_doc/biogpt title: BioGpt - local: model_doc/blenderbot title: Blenderbot - local: model_doc/blenderbot-small title: Blenderbot Small - local: model_doc/bloom title: BLOOM - local: model_doc/bort title: BORT - local: model_doc/byt5 title: ByT5 - local: model_doc/camembert title: CamemBERT - local: model_doc/canine title: CANINE - local: model_doc/codegen title: CodeGen - local: model_doc/convbert title: ConvBERT - local: model_doc/cpm title: CPM - local: model_doc/cpmant title: CPMANT - local: model_doc/ctrl title: CTRL - local: model_doc/deberta title: DeBERTa - local: model_doc/deberta-v2 title: DeBERTa-v2 - local: model_doc/dialogpt title: DialoGPT - local: model_doc/distilbert title: DistilBERT - local: model_doc/dpr title: DPR - local: model_doc/electra title: ELECTRA - local: model_doc/encoder-decoder title: Encoder Decoder Models - local: model_doc/ernie title: ERNIE - local: model_doc/ernie_m title: ErnieM - local: model_doc/esm title: ESM - local: model_doc/flan-t5 title: FLAN-T5 - local: model_doc/flan-ul2 title: FLAN-UL2 - local: model_doc/flaubert title: FlauBERT - local: model_doc/fnet title: FNet - local: model_doc/fsmt title: FSMT - local: model_doc/funnel title: Funnel Transformer - local: model_doc/openai-gpt title: GPT - local: model_doc/gpt_neo title: GPT Neo - local: model_doc/gpt_neox title: GPT NeoX - local: model_doc/gpt_neox_japanese title: GPT NeoX Japanese - local: model_doc/gptj title: GPT-J - local: model_doc/gpt2 title: GPT2 - local: model_doc/gpt_bigcode title: GPTBigCode - local: model_doc/gptsan-japanese title: GPTSAN Japanese - local: model_doc/gpt-sw3 title: GPTSw3 - local: model_doc/herbert title: HerBERT - local: model_doc/ibert title: I-BERT - local: model_doc/jukebox title: Jukebox - local: model_doc/led title: LED - local: model_doc/llama title: LLaMA - local: model_doc/longformer title: Longformer - local: model_doc/longt5 title: LongT5 - local: model_doc/luke title: LUKE - local: model_doc/m2m_100 title: M2M100 - local: model_doc/marian title: MarianMT - local: model_doc/markuplm title: MarkupLM - local: model_doc/mbart title: MBart and MBart-50 - local: model_doc/mega title: MEGA - local: model_doc/megatron-bert title: MegatronBERT - local: model_doc/megatron_gpt2 title: MegatronGPT2 - local: model_doc/mluke title: mLUKE - local: model_doc/mobilebert title: MobileBERT - local: model_doc/mpnet title: MPNet - local: model_doc/mt5 title: MT5 - local: model_doc/mvp title: MVP - local: model_doc/nezha title: NEZHA - local: model_doc/nllb title: NLLB - local: model_doc/nllb-moe title: NLLB-MoE - local: model_doc/nystromformer title: Nyströmformer - local: model_doc/open-llama title: Open-Llama - local: model_doc/opt title: OPT - local: model_doc/pegasus title: Pegasus - local: model_doc/pegasus_x title: PEGASUS-X - local: model_doc/phobert title: PhoBERT - local: model_doc/plbart title: PLBart - local: model_doc/prophetnet title: ProphetNet - local: model_doc/qdqbert title: QDQBert - local: model_doc/rag title: RAG - local: model_doc/realm title: REALM - local: model_doc/reformer title: Reformer - local: model_doc/rembert title: RemBERT - local: model_doc/retribert title: RetriBERT - local: model_doc/roberta title: RoBERTa - local: model_doc/roberta-prelayernorm title: RoBERTa-PreLayerNorm - local: model_doc/roc_bert title: RoCBert - local: model_doc/roformer title: RoFormer - local: model_doc/rwkv title: RWKV - local: model_doc/splinter title: Splinter - local: model_doc/squeezebert title: SqueezeBERT - local: model_doc/switch_transformers title: SwitchTransformers - local: model_doc/t5 title: T5 - local: model_doc/t5v1.1 title: T5v1.1 - local: model_doc/tapex title: TAPEX - local: model_doc/transfo-xl title: Transformer XL - local: model_doc/ul2 title: UL2 - local: model_doc/xmod title: X-MOD - local: model_doc/xglm title: XGLM - local: model_doc/xlm title: XLM - local: model_doc/xlm-prophetnet title: XLM-ProphetNet - local: model_doc/xlm-roberta title: XLM-RoBERTa - local: model_doc/xlm-roberta-xl title: XLM-RoBERTa-XL - local: model_doc/xlm-v title: XLM-V - local: model_doc/xlnet title: XLNet - local: model_doc/yoso title: YOSO title: Text models - isExpanded: false sections: - local: model_doc/beit title: BEiT - local: model_doc/bit title: BiT - local: model_doc/conditional_detr title: Conditional DETR - local: model_doc/convnext title: ConvNeXT - local: model_doc/convnextv2 title: ConvNeXTV2 - local: model_doc/cvt title: CvT - local: model_doc/deformable_detr title: Deformable DETR - local: model_doc/deit title: DeiT - local: model_doc/deta title: DETA - local: model_doc/detr title: DETR - local: model_doc/dinat title: DiNAT - local: model_doc/dit title: DiT - local: model_doc/dpt title: DPT - local: model_doc/efficientformer title: EfficientFormer - local: model_doc/efficientnet title: EfficientNet - local: model_doc/focalnet title: FocalNet - local: model_doc/glpn title: GLPN - local: model_doc/imagegpt title: ImageGPT - local: model_doc/levit title: LeViT - local: model_doc/mask2former title: Mask2Former - local: model_doc/maskformer title: MaskFormer - local: model_doc/mobilenet_v1 title: MobileNetV1 - local: model_doc/mobilenet_v2 title: MobileNetV2 - local: model_doc/mobilevit title: MobileViT - local: model_doc/nat title: NAT - local: model_doc/poolformer title: PoolFormer - local: model_doc/regnet title: RegNet - local: model_doc/resnet title: ResNet - local: model_doc/segformer title: SegFormer - local: model_doc/swiftformer title: SwiftFormer - local: model_doc/swin title: Swin Transformer - local: model_doc/swinv2 title: Swin Transformer V2 - local: model_doc/swin2sr title: Swin2SR - local: model_doc/table-transformer title: Table Transformer - local: model_doc/timesformer title: TimeSformer - local: model_doc/upernet title: UperNet - local: model_doc/van title: VAN - local: model_doc/videomae title: VideoMAE - local: model_doc/vit title: Vision Transformer (ViT) - local: model_doc/vit_hybrid title: ViT Hybrid - local: model_doc/vit_mae title: ViTMAE - local: model_doc/vit_msn title: ViTMSN - local: model_doc/yolos title: YOLOS title: Vision models - isExpanded: false sections: - local: model_doc/audio-spectrogram-transformer title: Audio Spectrogram Transformer - local: model_doc/clap title: CLAP - local: model_doc/hubert title: Hubert - local: model_doc/mctct title: MCTCT - local: model_doc/sew title: SEW - local: model_doc/sew-d title: SEW-D - local: model_doc/speech_to_text title: Speech2Text - local: model_doc/speech_to_text_2 title: Speech2Text2 - local: model_doc/speecht5 title: SpeechT5 - local: model_doc/unispeech title: UniSpeech - local: model_doc/unispeech-sat title: UniSpeech-SAT - local: model_doc/wav2vec2 title: Wav2Vec2 - local: model_doc/wav2vec2-conformer title: Wav2Vec2-Conformer - local: model_doc/wav2vec2_phoneme title: Wav2Vec2Phoneme - local: model_doc/wavlm title: WavLM - local: model_doc/whisper title: Whisper - local: model_doc/xls_r title: XLS-R - local: model_doc/xlsr_wav2vec2 title: XLSR-Wav2Vec2 title: Audio models - isExpanded: false sections: - local: model_doc/align title: ALIGN - local: model_doc/altclip title: AltCLIP - local: model_doc/blip title: BLIP - local: model_doc/blip-2 title: BLIP-2 - local: model_doc/bridgetower title: BridgeTower - local: model_doc/chinese_clip title: Chinese-CLIP - local: model_doc/clip title: CLIP - local: model_doc/clipseg title: CLIPSeg - local: model_doc/data2vec title: Data2Vec - local: model_doc/deplot title: DePlot - local: model_doc/donut title: Donut - local: model_doc/flava title: FLAVA - local: model_doc/git title: GIT - local: model_doc/groupvit title: GroupViT - local: model_doc/layoutlm title: LayoutLM - local: model_doc/layoutlmv2 title: LayoutLMV2 - local: model_doc/layoutlmv3 title: LayoutLMV3 - local: model_doc/layoutxlm title: LayoutXLM - local: model_doc/lilt title: LiLT - local: model_doc/lxmert title: LXMERT - local: model_doc/matcha title: MatCha - local: model_doc/mgp-str title: MGP-STR - local: model_doc/oneformer title: OneFormer - local: model_doc/owlvit title: OWL-ViT - local: model_doc/perceiver title: Perceiver - local: model_doc/pix2struct title: Pix2Struct - local: model_doc/sam title: Segment Anything - local: model_doc/speech-encoder-decoder title: Speech Encoder Decoder Models - local: model_doc/tapas title: TAPAS - local: model_doc/trocr title: TrOCR - local: model_doc/tvlt title: TVLT - local: model_doc/vilt title: ViLT - local: model_doc/vision-encoder-decoder title: Vision Encoder Decoder Models - local: model_doc/vision-text-dual-encoder title: Vision Text Dual Encoder - local: model_doc/visual_bert title: VisualBERT - local: model_doc/xclip title: X-CLIP title: Multimodal models - isExpanded: false sections: - local: model_doc/decision_transformer title: Decision Transformer - local: model_doc/trajectory_transformer title: Trajectory Transformer title: Reinforcement learning models - isExpanded: false sections: - local: model_doc/informer title: Informer - local: model_doc/time_series_transformer title: Time Series Transformer title: Time series models - isExpanded: false sections: - local: model_doc/graphormer title: Graphormer title: Graph models title: Models - sections: - local: internal/modeling_utils title: Custom Layers and Utilities - local: internal/pipelines_utils title: Utilities for pipelines - local: internal/tokenization_utils title: Utilities for Tokenizers - local: internal/trainer_utils title: Utilities for Trainer - local: internal/generation_utils title: Utilities for Generation - local: internal/image_processing_utils title: Utilities for Image Processors - local: internal/audio_utils title: Utilities for Audio processing - local: internal/file_utils title: General Utilities - local: internal/time_series_utils title: Utilities for Time Series title: Internal Helpers title: API

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# 共享自定义模型 🤗 Transformers 库设计得易于扩展。每个模型的代码都在仓库给定的子文件夹中，没有进行抽象，因此你可以轻松复制模型代码文件并根据需要进行调整。如果你要编写全新的模型，从头开始可能更容易。在本教程中，我们将向你展示如何编写自定义模型及其配置，以便可以在 Transformers 中使用它；以及如何与社区共享它（及其依赖的代码），以便任何人都可以使用，即使它不在 🤗 Transformers 库中。我们将以 ResNet 模型为例，通过将 [timm 库](https://github.com/rwightman/pytorch-image-models) 的 ResNet 类封装到 [`PreTrainedModel`] 中来进行说明。 ## 编写自定义配置在深入研究模型之前，让我们首先编写其配置。模型的配置是一个对象，其中包含构建模型所需的所有信息。我们将在下一节中看到，模型只能接受一个 `config` 来进行初始化，因此我们很需要使该对象尽可能完整。我们将采用一些我们可能想要调整的 ResNet 类的参数举例。不同的配置将为我们提供不同类型可能的 ResNet 模型。在确认其中一些参数的有效性后，我们只需存储这些参数。 ```python from transformers import PretrainedConfig from typing import List class ResnetConfig(PretrainedConfig): model_type = "resnet" def __init__( self, block_type="bottleneck", layers: List[int] = [3, 4, 6, 3], num_classes: int = 1000, input_channels: int = 3, cardinality: int = 1, base_width: int = 64, stem_width: int = 64, stem_type: str = "", avg_down: bool = False, **kwargs, ): if block_type not in ["basic", "bottleneck"]: raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.") if stem_type not in ["", "deep", "deep-tiered"]: raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.") self.block_type = block_type self.layers = layers self.num_classes = num_classes self.input_channels = input_channels self.cardinality = cardinality self.base_width = base_width self.stem_width = stem_width self.stem_type = stem_type self.avg_down = avg_down super().__init__(**kwargs) ``` 编写自定义配置时需要记住的三个重要事项如下： - 必须继承自 `PretrainedConfig`， - `PretrainedConfig` 的 `__init__` 方法必须接受任何 kwargs， - 这些 `kwargs` 需要传递给超类的 `__init__` 方法。继承是为了确保你获得来自 🤗 Transformers 库的所有功能，而另外两个约束源于 `PretrainedConfig` 的字段比你设置的字段多。在使用 `from_pretrained` 方法重新加载配置时，这些字段需要被你的配置接受，然后传递给超类。为你的配置定义 `model_type`（此处为 `model_type="resnet"`）不是必须的，除非你想使用自动类注册你的模型（请参阅最后一节）。做完这些以后，就可以像使用库里任何其他模型配置一样，轻松地创建和保存配置。以下代码展示了如何创建并保存 resnet50d 配置： ```py resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True) resnet50d_config.save_pretrained("custom-resnet") ``` 这行代码将在 `custom-resnet` 文件夹内保存一个名为 `config.json` 的文件。然后，你可以使用 `from_pretrained` 方法重新加载配置： ```py resnet50d_config = ResnetConfig.from_pretrained("custom-resnet") ``` 你还可以使用 [`PretrainedConfig`] 类的任何其他方法，例如 [`~PretrainedConfig.push_to_hub`]，直接将配置上传到 Hub。 ## 编写自定义模型有了 ResNet 配置后，就可以继续编写模型了。实际上，我们将编写两个模型：一个模型用于从一批图像中提取隐藏特征（类似于 [`BertModel`]），另一个模型适用于图像分类（类似于 [`BertForSequenceClassification`]）。正如之前提到的，我们只会编写一个松散的模型包装，以使示例保持简洁。在编写此类之前，只需要建立起块类型（block types）与实际块类（block classes）之间的映射。然后，通过将所有内容传递给ResNet类，从配置中定义模型： ```py from transformers import PreTrainedModel from timm.models.resnet import BasicBlock, Bottleneck, ResNet from .configuration_resnet import ResnetConfig BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck} class ResnetModel(PreTrainedModel): config_class = ResnetConfig def __init__(self, config): super().__init__(config) block_layer = BLOCK_MAPPING[config.block_type] self.model = ResNet( block_layer, config.layers, num_classes=config.num_classes, in_chans=config.input_channels, cardinality=config.cardinality, base_width=config.base_width, stem_width=config.stem_width, stem_type=config.stem_type, avg_down=config.avg_down, ) def forward(self, tensor): return self.model.forward_features(tensor) ``` 对用于进行图像分类的模型，我们只需更改前向方法： ```py import torch class ResnetModelForImageClassification(PreTrainedModel): config_class = ResnetConfig def __init__(self, config): super().__init__(config) block_layer = BLOCK_MAPPING[config.block_type] self.model = ResNet( block_layer, config.layers, num_classes=config.num_classes, in_chans=config.input_channels, cardinality=config.cardinality, base_width=config.base_width, stem_width=config.stem_width, stem_type=config.stem_type, avg_down=config.avg_down, ) def forward(self, tensor, labels=None): logits = self.model(tensor) if labels is not None: loss = torch.nn.cross_entropy(logits, labels) return {"loss": loss, "logits": logits} return {"logits": logits} ``` 在这两种情况下，请注意我们如何继承 `PreTrainedModel` 并使用 `config` 调用了超类的初始化（有点像编写常规的torch.nn.Module）。设置 `config_class` 的那行代码不是必须的，除非你想使用自动类注册你的模型（请参阅最后一节）。 <Tip> 如果你的模型与库中的某个模型非常相似，你可以重用与该模型相同的配置。 </Tip> 你可以让模型返回任何你想要的内容，但是像我们为 `ResnetModelForImageClassification` 做的那样返回一个字典，并在传递标签时包含loss，可以使你的模型能够在 [`Trainer`] 类中直接使用。只要你计划使用自己的训练循环或其他库进行训练，也可以使用其他输出格式。现在我们已经有了模型类，让我们创建一个： ```py resnet50d = ResnetModelForImageClassification(resnet50d_config) ``` 同样的，你可以使用 [`PreTrainedModel`] 的任何方法，比如 [`~PreTrainedModel.save_pretrained`] 或者 [`~PreTrainedModel.push_to_hub`]。我们将在下一节中使用第二种方法，并了解如何如何使用我们的模型的代码推送模型权重。但首先，让我们在模型内加载一些预训练权重。在你自己的用例中，你可能会在自己的数据上训练自定义模型。为了快速完成本教程，我们将使用 resnet50d 的预训练版本。由于我们的模型只是它的包装，转移这些权重将会很容易： ```py import timm pretrained_model = timm.create_model("resnet50d", pretrained=True) resnet50d.model.load_state_dict(pretrained_model.state_dict()) ``` 现在让我们看看，如何确保在执行 [`~PreTrainedModel.save_pretrained`] 或 [`~PreTrainedModel.push_to_hub`] 时，模型的代码被保存。 ## 将代码发送到 Hub <Tip warning={true}> 此 API 是实验性的，未来的发布中可能会有一些轻微的不兼容更改。 </Tip> 首先，确保你的模型在一个 `.py` 文件中完全定义。只要所有文件都位于同一目录中，它就可以依赖于某些其他文件的相对导入（目前我们还不为子模块支持此功能）。对于我们的示例，我们将在当前工作目录中名为 `resnet_model` 的文件夹中定义一个 `modeling_resnet.py` 文件和一个 `configuration_resnet.py` 文件。配置文件包含 `ResnetConfig` 的代码，模型文件包含 `ResnetModel` 和 `ResnetModelForImageClassification` 的代码。 ``` . └── resnet_model ├── __init__.py ├── configuration_resnet.py └── modeling_resnet.py ``` `__init__.py` 可以为空，它的存在只是为了让 Python 检测到 `resnet_model` 可以用作模块。 <Tip warning={true}> 如果从库中复制模型文件，你需要将文件顶部的所有相对导入替换为从 `transformers` 包中的导入。 </Tip> 请注意，你可以重用（或子类化）现有的配置/模型。要与社区共享您的模型，请参照以下步骤：首先从新创建的文件中导入ResNet模型和配置： ```py from resnet_model.configuration_resnet import ResnetConfig from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification ``` 接下来，你需要告诉库，当使用 `save_pretrained` 方法时，你希望复制这些对象的代码文件，并将它们正确注册到给定的 Auto 类（特别是对于模型），只需要运行以下代码： ```py ResnetConfig.register_for_auto_class() ResnetModel.register_for_auto_class("AutoModel") ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification") ``` 请注意，对于配置（只有一个自动类 [`AutoConfig`]），不需要指定自动类，但对于模型来说情况不同。你的自定义模型可能适用于许多不同的任务，因此你必须指定哪一个自动类适合你的模型。接下来，让我们像之前一样创建配置和模型： ```py resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True) resnet50d = ResnetModelForImageClassification(resnet50d_config) pretrained_model = timm.create_model("resnet50d", pretrained=True) resnet50d.model.load_state_dict(pretrained_model.state_dict()) ``` 现在要将模型推送到集线器，请确保你已登录。你看可以在终端中运行以下命令： ```bash huggingface-cli login ``` 或者在笔记本中运行以下代码： ```py from huggingface_hub import notebook_login notebook_login() ``` 然后，可以这样将模型推送到自己的命名空间（或你所属的组织）： ```py resnet50d.push_to_hub("custom-resnet50d") ``` 除了模型权重和 JSON 格式的配置外，这行代码也会复制 `custom-resnet50d` 文件夹内的模型以及配置的 `.py` 文件并将结果上传至 Hub。你可以在此[模型仓库](https://huggingface.co/sgugger/custom-resnet50d)中查看结果。有关推推送至 Hub 方法的更多信息，请参阅[共享教程](model_sharing)。 ## 使用带有自定义代码的模型可以使用自动类（auto-classes）和 `from_pretrained` 方法，使用模型仓库里带有自定义代码的配置、模型或分词器文件。所有上传到 Hub 的文件和代码都会进行恶意软件扫描（有关更多信息，请参阅 [Hub 安全](https://huggingface.co/docs/hub/security#malware-scanning) 文档）, 但你仍应查看模型代码和作者，以避免在你的计算机上执行恶意代码。设置 `trust_remote_code=True` 以使用带有自定义代码的模型： ```py from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True) ``` 我们强烈建议为 `revision` 参数传递提交哈希（commit hash），以确保模型的作者没有使用一些恶意的代码行更新了代码（除非您完全信任模型的作者）。 ```py commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292" model = AutoModelForImageClassification.from_pretrained( "sgugger/custom-resnet50d", trust_remote_code=True, revision=commit_hash ) ``` 在 Hub 上浏览模型仓库的提交历史时，有一个按钮可以轻松复制任何提交的提交哈希。 ## 将自定义代码的模型注册到自动类如果你在编写一个扩展 🤗 Transformers 的库，你可能想要扩展自动类以包含您自己的模型。这与将代码推送到 Hub 不同，因为用户需要导入你的库才能获取自定义模型（与从 Hub 自动下载模型代码相反）。只要你的配置 `model_type` 属性与现有模型类型不同，并且你的模型类有正确的 `config_class` 属性，你可以像这样将它们添加到自动类中： ```py from transformers import AutoConfig, AutoModel, AutoModelForImageClassification AutoConfig.register("resnet", ResnetConfig) AutoModel.register(ResnetConfig, ResnetModel) AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification) ``` 请注意，将自定义配置注册到 [`AutoConfig`] 时，使用的第一个参数需要与自定义配置的 `model_type` 匹配；而将自定义模型注册到任何自动模型类时，使用的第一个参数需要与 `config_class` 匹配。

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## 使用LLMs进行生成 [[open-in-colab]] LLMs，即大语言模型，是文本生成背后的关键组成部分。简单来说，它们包含经过大规模预训练的transformer模型，用于根据给定的输入文本预测下一个词（或更准确地说，下一个`token`）。由于它们一次只预测一个`token`，因此除了调用模型之外，您需要执行更复杂的操作来生成新的句子——您需要进行自回归生成。自回归生成是在给定一些初始输入，通过迭代调用模型及其自身的生成输出来生成文本的推理过程，。在🤗 Transformers中，这由[`~generation.GenerationMixin.generate`]方法处理，所有具有生成能力的模型都可以使用该方法。本教程将向您展示如何： * 使用LLM生成文本 * 避免常见的陷阱 * 帮助您充分利用LLM下一步指导在开始之前，请确保已安装所有必要的库： ```bash pip install transformers bitsandbytes>=0.39.0 -q ``` ## 生成文本一个用于[因果语言建模](tasks/language_modeling)训练的语言模型，将文本`tokens`序列作为输入，并返回下一个`token`的概率分布。  <figure class="image table text-center m-0 w-full"> <video style="max-width: 90%; margin: auto;" autoplay loop muted playsinline src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/assisted-generation/gif_1_1080p.mov" ></video> <figcaption>"LLM的前向传递"</figcaption> </figure> 使用LLM进行自回归生成的一个关键方面是如何从这个概率分布中选择下一个`token`。这个步骤可以随意进行，只要最终得到下一个迭代的`token`。这意味着可以简单的从概率分布中选择最可能的`token`，也可以复杂的在对结果分布进行采样之前应用多种变换，这取决于你的需求。  <figure class="image table text-center m-0 w-full"> <video style="max-width: 90%; margin: auto;" autoplay loop muted playsinline src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/assisted-generation/gif_2_1080p.mov" ></video> <figcaption>"自回归生成迭代地从概率分布中选择下一个token以生成文本"</figcaption> </figure> 上述过程是迭代重复的，直到达到某个停止条件。理想情况下，停止条件由模型决定，该模型应学会在何时输出一个结束序列（`EOS`）标记。如果不是这种情况，生成将在达到某个预定义的最大长度时停止。正确设置`token`选择步骤和停止条件对于让你的模型按照预期的方式执行任务至关重要。这就是为什么我们为每个模型都有一个[~generation.GenerationConfig]文件，它包含一个效果不错的默认生成参数配置，并与您模型一起加载。让我们谈谈代码！ <Tip> 如果您对基本的LLM使用感兴趣，我们高级的[`Pipeline`](pipeline_tutorial)接口是一个很好的起点。然而，LLMs通常需要像`quantization`和`token选择步骤的精细控制`等高级功能，这最好通过[`~generation.GenerationMixin.generate`]来完成。使用LLM进行自回归生成也是资源密集型的操作，应该在GPU上执行以获得足够的吞吐量。 </Tip> 首先，您需要加载模型。 ```py >>> from transformers import AutoModelForCausalLM >>> model = AutoModelForCausalLM.from_pretrained( ... "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True ... ) ``` 您将会注意到在`from_pretrained`调用中的两个标志： - `device_map`确保模型被移动到您的GPU(s)上 - `load_in_4bit`应用[4位动态量化](main_classes/quantization)来极大地减少资源需求还有其他方式来初始化一个模型，但这是一个开始使用LLM很好的起点。接下来，你需要使用一个[tokenizer](tokenizer_summary)来预处理你的文本输入。 ```py >>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left") >>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to("cuda") ``` `model_inputs`变量保存着分词后的文本输入以及注意力掩码。尽管[`~generation.GenerationMixin.generate`]在未传递注意力掩码时会尽其所能推断出注意力掩码，但建议尽可能传递它以获得最佳结果。在对输入进行分词后，可以调用[`~generation.GenerationMixin.generate`]方法来返回生成的`tokens`。生成的`tokens`应该在打印之前转换为文本。 ```py >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 'A list of colors: red, blue, green, yellow, orange, purple, pink,' ``` 最后，您不需要一次处理一个序列！您可以批量输入，这将在小延迟和低内存成本下显著提高吞吐量。您只需要确保正确地填充您的输入（详见下文）。 ```py >>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default >>> model_inputs = tokenizer( ... ["A list of colors: red, blue", "Portugal is"], return_tensors="pt", padding=True ... ).to("cuda") >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['A list of colors: red, blue, green, yellow, orange, purple, pink,', 'Portugal is a country in southwestern Europe, on the Iber'] ``` 就是这样！在几行代码中，您就可以利用LLM的强大功能。 ## 常见陷阱有许多[生成策略](generation_strategies)，有时默认值可能不适合您的用例。如果您的输出与您期望的结果不匹配，我们已经创建了一个最常见的陷阱列表以及如何避免它们。 ```py >>> from transformers import AutoModelForCausalLM, AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") >>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default >>> model = AutoModelForCausalLM.from_pretrained( ... "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True ... ) ``` ### 生成的输出太短/太长如果在[`~generation.GenerationConfig`]文件中没有指定，`generate`默认返回20个tokens。我们强烈建议在您的`generate`调用中手动设置`max_new_tokens`以控制它可以返回的最大新tokens数量。请注意，LLMs（更准确地说，仅[解码器模型](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt)）也将输入提示作为输出的一部分返回。 ```py >>> model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to("cuda") >>> # By default, the output will contain up to 20 tokens >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 'A sequence of numbers: 1, 2, 3, 4, 5' >>> # Setting `max_new_tokens` allows you to control the maximum length >>> generated_ids = model.generate(**model_inputs, max_new_tokens=50) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 'A sequence of numbers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,' ``` ### 错误的生成模式默认情况下，除非在[`~generation.GenerationConfig`]文件中指定，否则`generate`会在每个迭代中选择最可能的token（贪婪解码）。对于您的任务，这可能是不理想的；像聊天机器人或写作文章这样的创造性任务受益于采样。另一方面，像音频转录或翻译这样的基于输入的任务受益于贪婪解码。通过将`do_sample=True`启用采样，您可以在这篇[博客文章](https://huggingface.co/blog/how-to-generate)中了解更多关于这个话题的信息。 ```py >>> # Set seed or reproducibility -- you don't need this unless you want full reproducibility >>> from transformers import set_seed >>> set_seed(42) >>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to("cuda") >>> # LLM + greedy decoding = repetitive, boring output >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 'I am a cat. I am a cat. I am a cat. I am a cat' >>> # With sampling, the output becomes more creative! >>> generated_ids = model.generate(**model_inputs, do_sample=True) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] 'I am a cat. Specifically, I am an indoor-only cat. I' ``` ### 错误的填充位置 LLMs是[仅解码器](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt)架构，意味着它们会持续迭代您的输入提示。如果您的输入长度不相同，则需要对它们进行填充。由于LLMs没有接受过从`pad tokens`继续训练，因此您的输入需要左填充。确保在生成时不要忘记传递注意力掩码！ ```py >>> # The tokenizer initialized above has right-padding active by default: the 1st sequence, >>> # which is shorter, has padding on the right side. Generation fails to capture the logic. >>> model_inputs = tokenizer( ... ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt" ... ).to("cuda") >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] '1, 2, 33333333333' >>> # With left-padding, it works as expected! >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left") >>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default >>> model_inputs = tokenizer( ... ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt" ... ).to("cuda") >>> generated_ids = model.generate(**model_inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] '1, 2, 3, 4, 5, 6,' ``` ### 错误的提示一些模型和任务期望某种输入提示格式才能正常工作。当未应用此格式时，您将获得悄然的性能下降：模型能工作，但不如预期提示那样好。有关提示的更多信息，包括哪些模型和任务需要小心，可在[指南](tasks/prompting)中找到。让我们看一个使用[聊天模板](chat_templating)的聊天LLM示例： ```python >>> tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha") >>> model = AutoModelForCausalLM.from_pretrained( ... "HuggingFaceH4/zephyr-7b-alpha", device_map="auto", load_in_4bit=True ... ) >>> set_seed(0) >>> prompt = """How many helicopters can a human eat in one sitting? Reply as a thug.""" >>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda") >>> input_length = model_inputs.input_ids.shape[1] >>> generated_ids = model.generate(**model_inputs, max_new_tokens=20) >>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0]) "I'm not a thug, but i can tell you that a human cannot eat" >>> # Oh no, it did not follow our instruction to reply as a thug! Let's see what happens when we write >>> # a better prompt and use the right template for this model (through `tokenizer.apply_chat_template`) >>> set_seed(0) >>> messages = [ ... { ... "role": "system", ... "content": "You are a friendly chatbot who always responds in the style of a thug", ... }, ... {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ... ] >>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda") >>> input_length = model_inputs.shape[1] >>> generated_ids = model.generate(model_inputs, do_sample=True, max_new_tokens=20) >>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0]) 'None, you thug. How bout you try to focus on more useful questions?' >>> # As we can see, it followed a proper thug style 😎 ``` ## 更多资源虽然自回归生成过程相对简单，但要充分利用LLM可能是一个具有挑战性的任务，因为很多组件复杂且密切关联。以下是帮助您深入了解LLM使用和理解的下一步： ### 高级生成用法 1. [指南](generation_strategies)，介绍如何控制不同的生成方法、如何设置生成配置文件以及如何进行输出流式传输； 2. [指南](chat_templating)，介绍聊天LLMs的提示模板； 3. [指南](tasks/prompting)，介绍如何充分利用提示设计； 4. API参考文档，包括[`~generation.GenerationConfig`]、[`~generation.GenerationMixin.generate`]和[与生成相关的类](internal/generation_utils)。 ### LLM排行榜 1. [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), 侧重于开源模型的质量; 2. [Open LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard), 侧重于LLM的吞吐量. ### 延迟、吞吐量和内存利用率 1. [指南](llm_tutorial_optimization),如何优化LLMs以提高速度和内存利用； 2. [指南](main_classes/quantization), 关于`quantization`，如bitsandbytes和autogptq的指南，教您如何大幅降低内存需求。 ### 相关库 1. [`text-generation-inference`](https://github.com/huggingface/text-generation-inference), 一个面向生产的LLM服务器； 2. [`optimum`](https://github.com/huggingface/optimum), 一个🤗 Transformers的扩展，优化特定硬件设备的性能

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# 量化 🤗 Transformers 模型 ## AWQ集成 AWQ方法已经在[*AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration*论文](https://arxiv.org/abs/2306.00978)中引入。通过AWQ，您可以以4位精度运行模型，同时保留其原始性能（即没有性能降级），并具有比下面介绍的其他量化方法更出色的吞吐量 - 达到与纯`float16`推理相似的吞吐量。我们现在支持使用任何AWQ模型进行推理，这意味着任何人都可以加载和使用在Hub上推送或本地保存的AWQ权重。请注意，使用AWQ需要访问NVIDIA GPU。目前不支持CPU推理。 ### 量化一个模型我们建议用户查看生态系统中不同的现有工具，以使用AWQ算法对其模型进行量化，例如： - [`llm-awq`](https://github.com/mit-han-lab/llm-awq)，来自MIT Han Lab - [`autoawq`](https://github.com/casper-hansen/AutoAWQ)，来自[`casper-hansen`](https://github.com/casper-hansen) - Intel neural compressor，来自Intel - 通过[`optimum-intel`](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc)使用生态系统中可能存在许多其他工具，请随时提出PR将它们添加到列表中。目前与🤗 Transformers的集成仅适用于使用`autoawq`和`llm-awq`量化后的模型。大多数使用`auto-awq`量化的模型可以在🤗 Hub的[`TheBloke`](https://huggingface.co/TheBloke)命名空间下找到，要使用`llm-awq`对模型进行量化，请参阅[`llm-awq`](https://github.com/mit-han-lab/llm-awq/)的示例文件夹中的[`convert_to_hf.py`](https://github.com/mit-han-lab/llm-awq/blob/main/examples/convert_to_hf.py)脚本。 ### 加载一个量化的模型您可以使用`from_pretrained`方法从Hub加载一个量化模型。通过检查模型配置文件（`configuration.json`）中是否存在`quantization_config`属性，来进行确认推送的权重是量化的。您可以通过检查字段`quantization_config.quant_method`来确认模型是否以AWQ格式进行量化，该字段应该设置为`"awq"`。请注意，为了性能原因，默认情况下加载模型将设置其他权重为`float16`。如果您想更改这种设置，可以通过将`torch_dtype`参数设置为`torch.float32`或`torch.bfloat16`。在下面的部分中，您可以找到一些示例片段和notebook。 ## 示例使用首先，您需要安装[`autoawq`](https://github.com/casper-hansen/AutoAWQ)库 ```bash pip install autoawq ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "TheBloke/zephyr-7B-alpha-AWQ" model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:0") ``` 如果您首先将模型加载到CPU上，请确保在使用之前将其移动到GPU设备上。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "TheBloke/zephyr-7B-alpha-AWQ" model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda:0") ``` ### 结合 AWQ 和 Flash Attention 您可以将AWQ量化与Flash Attention结合起来，得到一个既被量化又更快速的模型。只需使用`from_pretrained`加载模型，并传递`attn_implementation="flash_attention_2"`参数。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ", attn_implementation="flash_attention_2", device_map="cuda:0") ``` ### 基准测试我们使用[`optimum-benchmark`](https://github.com/huggingface/optimum-benchmark)库进行了一些速度、吞吐量和延迟基准测试。请注意，在编写本文档部分时，可用的量化方法包括：`awq`、`gptq`和`bitsandbytes`。基准测试在一台NVIDIA-A100实例上运行，使用[`TheBloke/Mistral-7B-v0.1-AWQ`](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)作为AWQ模型，[`TheBloke/Mistral-7B-v0.1-GPTQ`](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ)作为GPTQ模型。我们还将其与`bitsandbytes`量化模型和`float16`模型进行了对比。以下是一些结果示例： <div style="text-align: center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/forward_memory_plot.png"> </div> <div style="text-align: center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/generate_memory_plot.png"> </div> <div style="text-align: center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/generate_throughput_plot.png"> </div> <div style="text-align: center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/quantization/forward_latency_plot.png"> </div> 你可以在[此链接](https://github.com/huggingface/optimum-benchmark/tree/main/examples/running-mistrals)中找到完整的结果以及包版本。从结果来看，AWQ量化方法是推理、文本生成中最快的量化方法，并且在文本生成的峰值内存方面属于最低。然而，对于每批数据，AWQ似乎有最大的前向延迟。 ### Google colab 演示查看如何在[Google Colab演示](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY)中使用此集成！ ### AwqConfig [[autodoc]] AwqConfig ## `AutoGPTQ` 集成 🤗 Transformers已经整合了`optimum` API，用于对语言模型执行GPTQ量化。您可以以8、4、3甚至2位加载和量化您的模型，而性能无明显下降，并且推理速度更快！这受到大多数GPU硬件的支持。要了解更多关于量化模型的信息，请查看： - [GPTQ](https://arxiv.org/pdf/2210.17323.pdf)论文 - `optimum`关于GPTQ量化的[指南](https://huggingface.co/docs/optimum/llm_quantization/usage_guides/quantization) - 用作后端的[`AutoGPTQ`](https://github.com/PanQiWei/AutoGPTQ)库 ### 要求为了运行下面的代码，您需要安装： - 安装最新版本的 `AutoGPTQ` 库 `pip install auto-gptq` - 从源代码安装最新版本的`optimum` `pip install git+https://github.com/huggingface/optimum.git` - 从源代码安装最新版本的`transformers` `pip install git+https://github.com/huggingface/transformers.git` - 安装最新版本的`accelerate`库： `pip install --upgrade accelerate` 请注意，目前GPTQ集成仅支持文本模型，对于视觉、语音或多模态模型可能会遇到预期以外结果。 ### 加载和量化模型 GPTQ是一种在使用量化模型之前需要进行权重校准的量化方法。如果您想从头开始对transformers模型进行量化，生成量化模型可能需要一些时间（在Google Colab上对`facebook/opt-350m`模型量化约为5分钟）。因此，有两种不同的情况下您可能想使用GPTQ量化模型。第一种情况是加载已经由其他用户在Hub上量化的模型，第二种情况是从头开始对您的模型进行量化并保存或推送到Hub，以便其他用户也可以使用它。 #### GPTQ 配置为了加载和量化一个模型，您需要创建一个[`GPTQConfig`]。您需要传递`bits`的数量，一个用于校准量化的`dataset`，以及模型的`tokenizer`以准备数据集。 ```python model_id = "facebook/opt-125m" tokenizer = AutoTokenizer.from_pretrained(model_id) gptq_config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer) ``` 请注意，您可以将自己的数据集以字符串列表形式传递到模型。然而，强烈建议您使用GPTQ论文中提供的数据集。 ```python dataset = ["auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."] quantization = GPTQConfig(bits=4, dataset = dataset, tokenizer=tokenizer) ``` #### 量化您可以通过使用`from_pretrained`并设置`quantization_config`来对模型进行量化。 ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=gptq_config) ``` 请注意，您需要一个GPU来量化模型。我们将模型放在cpu中，并将模块来回移动到gpu中，以便对其进行量化。如果您想在使用 CPU 卸载的同时最大化 GPU 使用率，您可以设置 `device_map = "auto"`。 ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config) ``` 请注意，不支持磁盘卸载。此外，如果由于数据集而内存不足，您可能需要在`from_pretrained`中设置`max_memory`。查看这个[指南](https://huggingface.co/docs/accelerate/usage_guides/big_modeling#designing-a-device-map)以了解有关`device_map`和`max_memory`的更多信息。 <Tip warning={true}> 目前，GPTQ量化仅适用于文本模型。此外，量化过程可能会花费很多时间，具体取决于硬件性能（175B模型在NVIDIA A100上需要4小时）。请在Hub上检查是否有模型的GPTQ量化版本。如果没有，您可以在GitHub上提交需求。 </Tip> ### 推送量化模型到 🤗 Hub 您可以使用`push_to_hub`将量化模型像任何模型一样推送到Hub。量化配置将与模型一起保存和推送。 ```python quantized_model.push_to_hub("opt-125m-gptq") tokenizer.push_to_hub("opt-125m-gptq") ``` 如果您想在本地计算机上保存量化模型，您也可以使用`save_pretrained`来完成： ```python quantized_model.save_pretrained("opt-125m-gptq") tokenizer.save_pretrained("opt-125m-gptq") ``` 请注意，如果您量化模型时想使用`device_map`，请确保在保存之前将整个模型移动到您的GPU或CPU之一。 ```python quantized_model.to("cpu") quantized_model.save_pretrained("opt-125m-gptq") ``` ### 从 🤗 Hub 加载一个量化模型您可以使用`from_pretrained`从Hub加载量化模型。请确保推送权重是量化的，检查模型配置对象中是否存在`quantization_config`属性。 ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq") ``` 如果您想更快地加载模型，并且不需要分配比实际需要内存更多的内存，量化模型也使用`device_map`参数。确保您已安装`accelerate`库。 ```python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto") ``` ### Exllama内核加快推理速度保留格式：对于 4 位模型，您可以使用 exllama 内核来提高推理速度。默认情况下，它处于启用状态。您可以通过在 [`GPTQConfig`] 中传递 `use_exllama` 来更改此配置。这将覆盖存储在配置中的量化配置。请注意，您只能覆盖与内核相关的属性。此外，如果您想使用 exllama 内核，整个模型需要全部部署在 gpus 上。此外，您可以使用版本 > 0.4.2 的 Auto-GPTQ 并传递 `device_map` = "cpu" 来执行 CPU 推理。对于 CPU 推理，您必须在 `GPTQConfig` 中传递 `use_exllama = False`。 ```py import torch gptq_config = GPTQConfig(bits=4) model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config=gptq_config) ``` 随着 exllamav2 内核的发布，与 exllama 内核相比，您可以获得更快的推理速度。您只需在 [`GPTQConfig`] 中传递 `exllama_config={"version": 2}`： ```py import torch gptq_config = GPTQConfig(bits=4, exllama_config={"version":2}) model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config = gptq_config) ``` 请注意，目前仅支持 4 位模型。此外，如果您正在使用 peft 对量化模型进行微调，建议禁用 exllama 内核。您可以在此找到这些内核的基准测试 [这里](https://github.com/huggingface/optimum/tree/main/tests/benchmark#gptq-benchmark) #### 微调一个量化模型在Hugging Face生态系统的官方支持下，您可以使用GPTQ进行量化后的模型进行微调。请查看`peft`库了解更多详情。 ### 示例演示请查看 Google Colab [notebook](https://colab.research.google.com/drive/1_TIrmuKOFhuRRiTWN94ilkUFu6ZX4ceb?usp=sharing)，了解如何使用GPTQ量化您的模型以及如何使用peft微调量化模型。 ### GPTQConfig [[autodoc]] GPTQConfig ## `bitsandbytes` 集成 🤗 Transformers 与 `bitsandbytes` 上最常用的模块紧密集成。您可以使用几行代码以 8 位精度加载您的模型。自bitsandbytes的0.37.0版本发布以来，大多数GPU硬件都支持这一点。在[LLM.int8()](https://arxiv.org/abs/2208.07339)论文中了解更多关于量化方法的信息，或者在[博客文章](https://huggingface.co/blog/hf-bitsandbytes-integration)中了解关于合作的更多信息。自其“0.39.0”版本发布以来，您可以使用FP4数据类型，通过4位量化加载任何支持“device_map”的模型。如果您想量化自己的 pytorch 模型，请查看 🤗 Accelerate 的[文档](https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization)。以下是您可以使用“bitsandbytes”集成完成的事情 ### 通用用法只要您的模型支持使用 🤗 Accelerate 进行加载并包含 `torch.nn.Linear` 层，您可以在调用 [`~PreTrainedModel.from_pretrained`] 方法时使用 `load_in_8bit` 或 `load_in_4bit` 参数来量化模型。这也应该适用于任何模态。 ```python from transformers import AutoModelForCausalLM model_8bit = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", load_in_8bit=True) model_4bit = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", load_in_4bit=True) ``` 默认情况下，所有其他模块（例如 `torch.nn.LayerNorm`）将被转换为 `torch.float16` 类型。但如果您想更改它们的 `dtype`，可以重载 `torch_dtype` 参数： ```python >>> import torch >>> from transformers import AutoModelForCausalLM >>> model_8bit = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", load_in_8bit=True, torch_dtype=torch.float32) >>> model_8bit.model.decoder.layers[-1].final_layer_norm.weight.dtype torch.float32 ``` ### FP4 量化 #### 要求确保在运行以下代码段之前已完成以下要求： - 最新版本 `bitsandbytes` 库 `pip install bitsandbytes>=0.39.0` - 安装最新版本 `accelerate` `pip install --upgrade accelerate` - 安装最新版本 `transformers` `pip install --upgrade transformers` #### 提示和最佳实践 - **高级用法：** 请参考 [此 Google Colab notebook](https://colab.research.google.com/drive/1ge2F1QSK8Q7h0hn3YKuBCOAS0bK8E0wf) 以获取 4 位量化高级用法和所有可选选项。 - **使用 `batch_size=1` 实现更快的推理：** 自 `bitsandbytes` 的 `0.40.0` 版本以来，设置 `batch_size=1`，您可以从快速推理中受益。请查看 [这些发布说明](https://github.com/TimDettmers/bitsandbytes/releases/tag/0.40.0) ，并确保使用大于 `0.40.0` 的版本以直接利用此功能。 - **训练：** 根据 [QLoRA 论文](https://arxiv.org/abs/2305.14314)，对于4位基模型训练（使用 LoRA 适配器），应使用 `bnb_4bit_quant_type='nf4'`。 - **推理：** 对于推理，`bnb_4bit_quant_type` 对性能影响不大。但是为了与模型的权重保持一致，请确保使用相同的 `bnb_4bit_compute_dtype` 和 `torch_dtype` 参数。 #### 加载 4 位量化的大模型在调用 `.from_pretrained` 方法时使用 `load_in_4bit=True`，可以将您的内存使用量减少到大约原来的 1/4。 ```python # pip install transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "bigscience/bloom-1b7" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True) ``` <Tip warning={true}> 需要注意的是，一旦模型以 4 位量化方式加载，就无法将量化后的权重推送到 Hub 上。此外，您不能训练 4 位量化权重，因为目前尚不支持此功能。但是，您可以使用 4 位量化模型来训练额外参数，这将在下一部分中介绍。 </Tip> ### 加载 8 位量化的大模型您可以通过在调用 `.from_pretrained` 方法时使用 `load_in_8bit=True` 参数，将内存需求大致减半来加载模型 ```python # pip install transformers accelerate bitsandbytes from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "bigscience/bloom-1b7" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_8bit=True) ``` 然后，像通常使用 `PreTrainedModel` 一样使用您的模型。您可以使用 `get_memory_footprint` 方法检查模型的内存占用。 ```python print(model.get_memory_footprint()) ``` 通过这种集成，我们能够在较小的设备上加载大模型并运行它们而没有任何问题。 <Tip warning={true}> 需要注意的是，一旦模型以 8 位量化方式加载，除了使用最新的 `transformers` 和 `bitsandbytes` 之外，目前尚无法将量化后的权重推送到 Hub 上。此外，您不能训练 8 位量化权重，因为目前尚不支持此功能。但是，您可以使用 8 位量化模型来训练额外参数，这将在下一部分中介绍。注意，`device_map` 是可选的，但设置 `device_map = 'auto'` 更适合用于推理，因为它将更有效地调度可用资源上的模型。 </Tip> #### 高级用例在这里，我们将介绍使用 FP4 量化的一些高级用例。 ##### 更改计算数据类型计算数据类型用于改变在进行计算时使用的数据类型。例如，hidden states可以是 `float32`，但为了加速，计算时可以被设置为 `bf16`。默认情况下，计算数据类型被设置为 `float32`。 ```python import torch from transformers import BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16) ``` #### 使用 NF4（普通浮点数 4）数据类型您还可以使用 NF4 数据类型，这是一种针对使用正态分布初始化的权重而适应的新型 4 位数据类型。要运行： ```python from transformers import BitsAndBytesConfig nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", ) model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config) ``` #### 使用嵌套量化进行更高效的内存推理我们还建议用户使用嵌套量化技术。从我们的经验观察来看，这种方法在不增加额外性能的情况下节省更多内存。这使得 llama-13b 模型能够在具有 1024 个序列长度、1 个批次大小和 4 个梯度累积步骤的 NVIDIA-T4 16GB 上进行 fine-tuning。 ```python from transformers import BitsAndBytesConfig double_quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, ) model_double_quant = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=double_quant_config) ``` ### 将量化模型推送到🤗 Hub 您可以使用 `push_to_hub` 方法将量化模型推送到 Hub 上。这将首先推送量化配置文件，然后推送量化模型权重。请确保使用 `bitsandbytes>0.37.2`（在撰写本文时，我们使用的是 `bitsandbytes==0.38.0.post1`）才能使用此功能。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m", device_map="auto", load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m") model.push_to_hub("bloom-560m-8bit") ``` <Tip warning={true}> 对大模型，强烈鼓励将 8 位量化模型推送到 Hub 上，以便让社区能够从内存占用减少和加载中受益，例如在 Google Colab 上加载大模型。 </Tip> ### 从🤗 Hub加载量化模型您可以使用 `from_pretrained` 方法从 Hub 加载量化模型。请确保推送的权重是量化的，检查模型配置对象中是否存在 `quantization_config` 属性。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit", device_map="auto") ``` 请注意，在这种情况下，您不需要指定 `load_in_8bit=True` 参数，但需要确保 `bitsandbytes` 和 `accelerate` 已安装。情注意，`device_map` 是可选的，但设置 `device_map = 'auto'` 更适合用于推理，因为它将更有效地调度可用资源上的模型。 ### 高级用例本节面向希望探索除了加载和运行 8 位模型之外还能做什么的进阶用户。 #### 在 `cpu` 和 `gpu` 之间卸载此高级用例之一是能够加载模型并将权重分派到 `CPU` 和 `GPU` 之间。请注意，将在 CPU 上分派的权重 **不会** 转换为 8 位，因此会保留为 `float32`。此功能适用于想要适应非常大的模型并将模型分派到 GPU 和 CPU 之间的用户。首先，从 `transformers` 中加载一个 [`BitsAndBytesConfig`]，并将属性 `llm_int8_enable_fp32_cpu_offload` 设置为 `True`： ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True) ``` 假设您想加载 `bigscience/bloom-1b7` 模型，您的 GPU显存仅足够容纳除了`lm_head`外的整个模型。因此，您可以按照以下方式编写自定义的 device_map： ```python device_map = { "transformer.word_embeddings": 0, "transformer.word_embeddings_layernorm": 0, "lm_head": "cpu", "transformer.h": 0, "transformer.ln_f": 0, } ``` 然后如下加载模型： ```python model_8bit = AutoModelForCausalLM.from_pretrained( "bigscience/bloom-1b7", device_map=device_map, quantization_config=quantization_config, ) ``` 这就是全部内容！享受您的模型吧！ #### 使用`llm_int8_threshold` 您可以使用 `llm_int8_threshold` 参数来更改异常值的阈值。“异常值”是一个大于特定阈值的`hidden state`值。这对应于`LLM.int8()`论文中描述的异常检测的异常阈值。任何高于此阈值的`hidden state`值都将被视为异常值，对这些值的操作将在 fp16 中完成。值通常是正态分布的，也就是说，大多数值在 [-3.5, 3.5] 范围内，但有一些额外的系统异常值，对于大模型来说，它们的分布非常不同。这些异常值通常在区间 [-60, -6] 或 [6, 60] 内。Int8 量化对于幅度为 ~5 的值效果很好，但超出这个范围，性能就会明显下降。一个好的默认阈值是 6，但对于更不稳定的模型（小模型、微调）可能需要更低的阈值。这个参数会影响模型的推理速度。我们建议尝试这个参数，以找到最适合您的用例的参数。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "bigscience/bloom-1b7" quantization_config = BitsAndBytesConfig( llm_int8_threshold=10, ) model_8bit = AutoModelForCausalLM.from_pretrained( model_id, device_map=device_map, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_id) ``` #### 跳过某些模块的转换一些模型有几个需要保持未转换状态以确保稳定性的模块。例如，Jukebox 模型有几个 `lm_head` 模块需要跳过。使用 `llm_int8_skip_modules` 参数进行相应操作。 ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig model_id = "bigscience/bloom-1b7" quantization_config = BitsAndBytesConfig( llm_int8_skip_modules=["lm_head"], ) model_8bit = AutoModelForCausalLM.from_pretrained( model_id, device_map=device_map, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_id) ``` #### 微调已加载为8位精度的模型借助Hugging Face生态系统中适配器（adapters）的官方支持，您可以在8位精度下微调模型。这使得可以在单个Google Colab中微调大模型，例如`flan-t5-large`或`facebook/opt-6.7b`。请查看[`peft`](https://github.com/huggingface/peft)库了解更多详情。注意，加载模型进行训练时无需传递`device_map`。它将自动将您的模型加载到GPU上。如果需要，您可以将设备映射为特定设备（例如`cuda:0`、`0`、`torch.device('cuda:0')`）。请注意，`device_map=auto`仅应用于推理。 ### BitsAndBytesConfig [[autodoc]] BitsAndBytesConfig ## 使用 🤗 `optimum` 进行量化请查看[Optimum 文档](https://huggingface.co/docs/optimum/index)以了解更多关于`optimum`支持的量化方法，并查看这些方法是否适用于您的用例。

transformers/docs/source/zh/main_classes/quantization.md/0

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# 自动语音识别 [[open-in-colab]] <Youtube id="TksaY_FDgnk"/> 自动语音识别（ASR）将语音信号转换为文本，将一系列音频输入映射到文本输出。 Siri 和 Alexa 这类虚拟助手使用 ASR 模型来帮助用户日常生活，还有许多其他面向用户的有用应用，如会议实时字幕和会议纪要。本指南将向您展示如何： 1. 在 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 数据集上对 [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) 进行微调，以将音频转录为文本。 2. 使用微调后的模型进行推断。 <Tip> 本教程中展示的任务受以下模型架构的支持：  [Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)  </Tip> 在开始之前，请确保您已安装所有必要的库： ```bash pip install transformers datasets evaluate jiwer ``` 我们鼓励您登录自己的 Hugging Face 账户，这样您就可以上传并与社区分享您的模型。出现提示时，输入您的令牌登录： ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## 加载 MInDS-14 数据集首先从🤗 Datasets 库中加载 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 数据集的一个较小子集。这将让您有机会先进行实验，确保一切正常，然后再花更多时间在完整数据集上进行训练。 ```py >>> from datasets import load_dataset, Audio >>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]") ``` 使用 [`~Dataset.train_test_split`] 方法将数据集的 `train` 拆分为训练集和测试集： ```py >>> minds = minds.train_test_split(test_size=0.2) ``` 然后看看数据集： ```py >>> minds DatasetDict({ train: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 16 }) test: Dataset({ features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'], num_rows: 4 }) }) ``` 虽然数据集包含 `lang_id `和 `english_transcription` 等许多有用的信息，但在本指南中，您将专注于 `audio` 和 `transcription`。使用 [`~datasets.Dataset.remove_columns`] 方法删除其他列： ```py >>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"]) ``` 再看看示例： ```py >>> minds["train"][0] {'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414, 0.00024414, 0.00024414], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'sampling_rate': 8000}, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"} ``` 有 2 个字段： - `audio`：由语音信号形成的一维 `array`，用于加载和重新采样音频文件。 - `transcription`：目标文本。 ## 预处理下一步是加载一个 Wav2Vec2 处理器来处理音频信号： ```py >>> from transformers import AutoProcessor >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base") ``` MInDS-14 数据集的采样率为 8000kHz（您可以在其[数据集卡片](https://huggingface.co/datasets/PolyAI/minds14)中找到此信息），这意味着您需要将数据集重新采样为 16000kHz 以使用预训练的 Wav2Vec2 模型： ```py >>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000)) >>> minds["train"][0] {'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ..., 2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32), 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'sampling_rate': 16000}, 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav', 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"} ``` 如您在上面的 `transcription` 中所看到的，文本包含大小写字符的混合。 Wav2Vec2 分词器仅训练了大写字符，因此您需要确保文本与分词器的词汇表匹配： ```py >>> def uppercase(example): ... return {"transcription": example["transcription"].upper()} >>> minds = minds.map(uppercase) ``` 现在创建一个预处理函数，该函数应该： 1. 调用 `audio` 列以加载和重新采样音频文件。 2. 从音频文件中提取 `input_values` 并使用处理器对 `transcription` 列执行 tokenizer 操作。 ```py >>> def prepare_dataset(batch): ... audio = batch["audio"] ... batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"]) ... batch["input_length"] = len(batch["input_values"][0]) ... return batch ``` 要在整个数据集上应用预处理函数，可以使用🤗 Datasets 的 [`~datasets.Dataset.map`] 函数。您可以通过增加 `num_proc` 参数来加速 `map` 的处理进程数量。使用 [`~datasets.Dataset.remove_columns`] 方法删除不需要的列： ```py >>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4) ``` 🤗 Transformers 没有用于 ASR 的数据整理器，因此您需要调整 [`DataCollatorWithPadding`] 来创建一个示例批次。它还会动态地将您的文本和标签填充到其批次中最长元素的长度（而不是整个数据集），以使它们具有统一的长度。虽然可以通过在 `tokenizer` 函数中设置 `padding=True` 来填充文本，但动态填充更有效。与其他数据整理器不同，这个特定的数据整理器需要对 `input_values` 和 `labels `应用不同的填充方法： ```py >>> import torch >>> from dataclasses import dataclass, field >>> from typing import Any, Dict, List, Optional, Union >>> @dataclass ... class DataCollatorCTCWithPadding: ... processor: AutoProcessor ... padding: Union[bool, str] = "longest" ... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: ... # split inputs and labels since they have to be of different lengths and need ... # different padding methods ... input_features = [{"input_values": feature["input_values"][0]} for feature in features] ... label_features = [{"input_ids": feature["labels"]} for feature in features] ... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt") ... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt") ... # replace padding with -100 to ignore loss correctly ... labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) ... batch["labels"] = labels ... return batch ``` 现在实例化您的 `DataCollatorForCTCWithPadding`： ```py >>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest") ``` ## 评估在训练过程中包含一个指标通常有助于评估模型的性能。您可以通过🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 库快速加载一个评估方法。对于这个任务，加载 [word error rate](https://huggingface.co/spaces/evaluate-metric/wer)（WER）指标（请参阅🤗 Evaluate [快速上手](https://huggingface.co/docs/evaluate/a_quick_tour)以了解如何加载和计算指标）： ```py >>> import evaluate >>> wer = evaluate.load("wer") ``` 然后创建一个函数，将您的预测和标签传递给 [`~evaluate.EvaluationModule.compute`] 来计算 WER： ```py >>> import numpy as np >>> def compute_metrics(pred): ... pred_logits = pred.predictions ... pred_ids = np.argmax(pred_logits, axis=-1) ... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id ... pred_str = processor.batch_decode(pred_ids) ... label_str = processor.batch_decode(pred.label_ids, group_tokens=False) ... wer = wer.compute(predictions=pred_str, references=label_str) ... return {"wer": wer} ``` 您的 `compute_metrics` 函数现在已经准备就绪，当您设置好训练时将返回给此函数。 ## 训练 <frameworkcontent> <pt> <Tip> 如果您不熟悉使用[`Trainer`]微调模型，请查看这里的基本教程[here](../training#train-with-pytorch-trainer)！ </Tip> 现在您已经准备好开始训练您的模型了！使用 [`AutoModelForCTC`] 加载 Wav2Vec2。使用 `ctc_loss_reduction` 参数指定要应用的减少方式。通常最好使用平均值而不是默认的求和： ```py >>> from transformers import AutoModelForCTC, TrainingArguments, Trainer >>> model = AutoModelForCTC.from_pretrained( ... "facebook/wav2vec2-base", ... ctc_loss_reduction="mean", ... pad_token_id=processor.tokenizer.pad_token_id, ) ``` 此时，只剩下 3 个步骤： 1. 在 [`TrainingArguments`] 中定义您的训练参数。唯一必需的参数是 `output_dir`，用于指定保存模型的位置。您可以通过设置 `push_to_hub=True` 将此模型推送到 Hub（您需要登录到 Hugging Face 才能上传您的模型）。在每个 epoch 结束时，[`Trainer`] 将评估 WER 并保存训练检查点。 2. 将训练参数与模型、数据集、分词器、数据整理器和 `compute_metrics` 函数一起传递给 [`Trainer`]。 3. 调用 [`~Trainer.train`] 来微调您的模型。 ```py >>> training_args = TrainingArguments( ... output_dir="my_awesome_asr_mind_model", ... per_device_train_batch_size=8, ... gradient_accumulation_steps=2, ... learning_rate=1e-5, ... warmup_steps=500, ... max_steps=2000, ... gradient_checkpointing=True, ... fp16=True, ... group_by_length=True, ... evaluation_strategy="steps", ... per_device_eval_batch_size=8, ... save_steps=1000, ... eval_steps=1000, ... logging_steps=25, ... load_best_model_at_end=True, ... metric_for_best_model="wer", ... greater_is_better=False, ... push_to_hub=True, ... ) >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=encoded_minds["train"], ... eval_dataset=encoded_minds["test"], ... tokenizer=processor, ... data_collator=data_collator, ... compute_metrics=compute_metrics, ... ) >>> trainer.train() ``` 训练完成后，使用 [`~transformers.Trainer.push_to_hub`] 方法将您的模型分享到 Hub，方便大家使用您的模型： ```py >>> trainer.push_to_hub() ``` </pt> </frameworkcontent> <Tip> 要深入了解如何微调模型进行自动语音识别，请查看这篇博客[文章](https://huggingface.co/blog/fine-tune-wav2vec2-english)以了解英语 ASR，还可以参阅[这篇文章](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)以了解多语言 ASR。 </Tip> ## 推断很好，现在您已经微调了一个模型，您可以用它进行推断了！加载您想要运行推断的音频文件。请记住，如果需要，将音频文件的采样率重新采样为与模型匹配的采样率！ ```py >>> from datasets import load_dataset, Audio >>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train") >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000)) >>> sampling_rate = dataset.features["audio"].sampling_rate >>> audio_file = dataset[0]["audio"]["path"] ``` 尝试使用微调后的模型进行推断的最简单方法是使用 [`pipeline`]。使用您的模型实例化一个用于自动语音识别的 `pipeline`，并将您的音频文件传递给它： ```py >>> from transformers import pipeline >>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model") >>> transcriber(audio_file) {'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'} ``` <Tip> 转录结果还不错，但可以更好！尝试用更多示例微调您的模型，以获得更好的结果！ </Tip> 如果您愿意，您也可以手动复制 `pipeline` 的结果： <frameworkcontent> <pt> 加载一个处理器来预处理音频文件和转录，并将 `input` 返回为 PyTorch 张量： ```py >>> from transformers import AutoProcessor >>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model") >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") ``` 将您的输入传递给模型并返回 logits： ```py >>> from transformers import AutoModelForCTC >>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model") >>> with torch.no_grad(): ... logits = model(**inputs).logits ``` 获取具有最高概率的预测 `input_ids`，并使用处理器将预测的 `input_ids` 解码回文本： ```py >>> import torch >>> predicted_ids = torch.argmax(logits, dim=-1) >>> transcription = processor.batch_decode(predicted_ids) >>> transcription ['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'] ``` </pt> </frameworkcontent>

transformers/docs/source/zh/tasks/asr.md/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 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. """ Pretraining the library models for denoising language modeling on a text file or a dataset. Here is the full list of checkpoints on the hub that can be pretrained by this script: https://huggingface.co/models?filter=bart """ # You can also adapt this script on your own denoising language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys import time import warnings from dataclasses import asdict, dataclass, field from enum import Enum from itertools import chain from pathlib import Path from typing import Dict, List, Optional import flax import jax import jax.numpy as jnp import nltk import numpy as np import optax from datasets import load_dataset from flax import jax_utils, traverse_util from flax.jax_utils import pad_shard_unpad from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from huggingface_hub import HfApi from tqdm import tqdm from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoTokenizer, BartConfig, BatchEncoding, FlaxBartForConditionalGeneration, HfArgumentParser, PreTrainedTokenizerBase, is_tensorboard_available, set_seed, ) from transformers.models.bart.modeling_flax_bart import shift_tokens_right from transformers.utils import send_example_telemetry MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class TrainingArguments: output_dir: str = field( metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, ) overwrite_output_dir: bool = field( default=False, metadata={ "help": ( "Overwrite the content of the output directory. " "Use this to continue training if output_dir points to a checkpoint directory." ) }, ) do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) per_device_train_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} ) per_device_eval_batch_size: int = field( default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} ) learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) push_to_hub: bool = field( default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} ) hub_model_id: str = field( default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} ) hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) def __post_init__(self): if self.output_dir is not None: self.output_dir = os.path.expanduser(self.output_dir) def to_dict(self): """ Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value. """ d = asdict(self) for k, v in d.items(): if isinstance(v, Enum): d[k] = v.value if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): d[k] = [x.value for x in v] if k.endswith("_token"): d[k] = f"<{k.upper()}>" return d @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization and masking. Sequences longer than this" " will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"} ) permute_sentence_ratio: float = field( default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"} ) poisson_lambda: float = field( default=3.0, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"} ) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("train_file` should be a csv, json or text file.") if self.validation_file is not None: extension = self.validation_file.split(".")[-1] if extension not in ["csv", "json", "txt"]: raise ValueError("`validation_file` should be a csv, json or text file.") @flax.struct.dataclass class FlaxDataCollatorForBartDenoisingLM: """ Data collator used for BART denoising language modeling. The code is largely copied from `<https://github.com/morganmcg1/rotobart/blob/main/data_collator.py#L223>`__. For more information on how BART denoising language modeling works, one can take a look at the `official paper <https://arxiv.org/pdf/1910.13461.pdf>`__ or the `official code for preprocessing <https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/denoising_dataset.py>`__ . Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data mask_ratio (:obj:`float`): The probability with which to (randomly) mask tokens in the input poisson_lambda (:obj:`float`): Mean parameter of Poisson distribution used to generate span-lengths to be masked permute_sentence_ratio (:obj:`float`): Ratio of sentences to be permuted in each document decoder_start_token_id: (:obj:`int): The decoder start token id of the model """ tokenizer: PreTrainedTokenizerBase decoder_start_token_id: int mask_ratio: float = 0.3 poisson_lambda: float = 3.0 permute_sentence_ratio: float = 1.0 def __post_init__(self): if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None: raise ValueError( "This tokenizer does not have a mask token or eos token token which is necessary for denoising" " language modeling. " ) def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding: # convert list to dict and tensorize input batch = BatchEncoding( {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()} ) batch["labels"] = batch["input_ids"].copy() batch["decoder_input_ids"] = shift_tokens_right( batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id ) # permuting sentences do_permute = False if self.permute_sentence_ratio > 0.0: batch["input_ids"] = self.permute_sentences(batch["input_ids"]) do_permute = True # masking span of tokens (text infilling in the paper) if self.mask_ratio: batch["input_ids"], batch["labels"] = self.span_mask_tokens( batch["input_ids"], batch["labels"], do_permute ) # ignore pad tokens batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int) batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int) return batch def permute_sentences(self, input_ids): """ Shuffle sentences in each document. """ results = input_ids.copy() # find end locations of sentences end_sentence_mask = input_ids == self.tokenizer.pad_token_id sentence_ends = np.argwhere(end_sentence_mask) sentence_ends[:, 1] += 1 example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True) num_sentences_map = dict(zip(example_has_multiple_sentences, num_sentences)) num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int) num_to_permute_map = dict(zip(example_has_multiple_sentences, num_to_permute)) sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:]) sentence_ends_map = dict(zip(example_has_multiple_sentences, sentence_ends)) for i in range(input_ids.shape[0]): if i not in example_has_multiple_sentences: continue substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]] ordering = np.arange(0, num_sentences_map[i]) ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])] # write shuffled sentences into results index = 0 for j in ordering: sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]] results[i, index : index + sentence.shape[0]] = sentence index += sentence.shape[0] return results def span_mask_tokens(self, input_ids, labels, do_permute): """ Sampling text spans with span lengths drawn from a Poisson distribution and masking them. """ special_tokens_mask_labels = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() ] special_tokens_mask_inputs = [ self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist() ] special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool) special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool) # determine how many tokens we need to mask in total is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio)) if num_tokens_to_mask == 0: return input_ids, labels # generate a sufficient number of span lengths span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,)) while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask: span_lengths = np.concatenate( [span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))] ) # remove all spans of length 0 # note that BART inserts additional mask tokens where length == 0, # which we do not implement for now as it adds additional complexity span_lengths = span_lengths[span_lengths > 0] # trim to about num_tokens_to_mask tokens cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1 span_lengths = span_lengths[:cutoff_idx] # randomly choose starting positions for masking token_indices = np.argwhere(is_token_mask == 1) span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]] # prepare mask masked_indices = np.array(token_indices[span_starts]) mask = np.full_like(input_ids, fill_value=False) # mask starting positions for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 # fill up spans max_index = input_ids.shape[1] - 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) while np.any(remaining): masked_indices[remaining, 1] += 1 for mi in masked_indices: mask[tuple(mi)] = True span_lengths -= 1 remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) # place the mask tokens mask[np.where(special_tokens_mask_inputs)] = False input_ids[np.where(mask)] = self.tokenizer.mask_token_id if not do_permute: labels[np.where(mask == 0)] = -100 else: labels[np.where(special_tokens_mask_labels)] = -100 # remove mask tokens that are not starts of spans to_remove = (mask == 1) & np.roll((mask == 1), 1, 1) new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id) for i, example in enumerate(input_ids): new_example = example[~to_remove[i]] new_input_ids[i, : new_example.shape[0]] = new_example return new_input_ids, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray: """Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned.""" num_samples = len(samples_idx) if drop_last: samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size samples_idx = samples_idx.reshape((sections_split, batch_size)) else: sections_split = math.ceil(num_samples / batch_size) samples_idx = np.array_split(samples_idx, sections_split) return samples_idx def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_bart_dlm", model_args, data_args, framework="flax") if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.INFO, datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Handle the repository creation if training_args.push_to_hub: # Retrieve of infer repo_name repo_name = training_args.hub_model_id if repo_name is None: repo_name = Path(training_args.output_dir).absolute().name # Create repo and retrieve repo_id api = HfApi() repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( extension, data_files=data_files, split=f"train[:{data_args.validation_split_percentage}%]", cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) datasets["train"] = load_dataset( extension, data_files=data_files, split=f"train[{data_args.validation_split_percentage}%:]", cache_dir=model_args.cache_dir, token=model_args.token, num_proc=data_args.preprocessing_num_workers, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets. # Load pretrained model and tokenizer if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, token=model_args.token, ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, token=model_args.token, ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script. " "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.config_name: config = BartConfig.from_pretrained( model_args.config_name, cache_dir=model_args.cache_dir, vocab_size=len(tokenizer), token=model_args.token, ) elif model_args.model_name_or_path: config = BartConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, token=model_args.token, ) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) # Use Punkt Sentence Tokenizer to divide a document into a list of sentences nltk.download("punkt") sentence_tokenizer = nltk.data.load("tokenizers/punkt/english.pickle") def sentence_split_function(example): sents = sentence_tokenizer.tokenize(example["text"]) # use pad token as end of sentence indicator new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token return {"text": new_text} split_datasets = datasets.map( sentence_split_function, batched=False, num_proc=data_args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not data_args.overwrite_cache, ) # Tokenize every text, then concatenate them together before splitting them in smaller parts. # Since we make sure that all sequences are of the same length, no attention_mask is needed. def tokenize_function(examples): return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False) tokenized_datasets = split_datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=text_column_name, load_from_cache_file=not data_args.overwrite_cache, ) # Main data processing function that will concatenate all texts from our dataset and generate chunks of # max_seq_length. def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= max_seq_length: total_length = (total_length // max_seq_length) * max_seq_length # Split by chunks of max_len. result = { k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] for k, t in concatenated_examples.items() } return result # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value # might be slower to preprocess. # # To speed up this part, we use multiprocessing. See the documentation of the map method for more information: # https://huggingface.co/docs/datasets/process#map tokenized_datasets = tokenized_datasets.map( group_texts, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, ) # Enable tensorboard only on the master node has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) else: logger.warning( "Unable to display metrics through TensorBoard because the package is not installed: " "Please run pip install tensorboard to enable." ) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxBartForConditionalGeneration.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), token=model_args.token, ) else: config.vocab_size = len(tokenizer) model = FlaxBartForConditionalGeneration( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype), ) # Data collator # This one will take care of randomly masking the tokens and permuting the sentences. data_collator = FlaxDataCollatorForBartDenoisingLM( tokenizer=tokenizer, decoder_start_token_id=model.config.decoder_start_token_id, mask_ratio=data_args.mlm_probability, poisson_lambda=data_args.poisson_lambda, permute_sentence_ratio=data_args.permute_sentence_ratio, ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() per_device_eval_batch_size = int(training_args.per_device_eval_batch_size) eval_batch_size = per_device_eval_batch_size * jax.device_count() num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) # find out all LayerNorm parameters layer_norm_candidates = ["layernorm", "layer_norm", "ln"] layer_norm_named_params = { layer[-2:] for layer_norm_name in layer_norm_candidates for layer in flat_params.keys() if layer_norm_name in "".join(layer).lower() } flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer if training_args.adafactor: # We use the default parameters here to initialize adafactor, # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74 optimizer = optax.adafactor( learning_rate=linear_decay_lr_schedule_fn, ) else: optimizer = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() num_labels = label_mask.sum() return loss, num_labels grad_fn = jax.value_and_grad(loss_fn, has_aux=True) (loss, num_labels), grad = grad_fn(state.params) num_labels = jax.lax.psum(num_labels, "batch") # true loss = total loss / total samples loss = jax.lax.psum(loss, "batch") loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) # true grad = total grad / total samples grad = jax.lax.psum(grad, "batch") grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad) new_state = state.apply_gradients(grads=grad) metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens and special tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 epochs = tqdm(range(num_epochs), desc="Epoch ... ", position=0) for epoch in epochs: # ======================== Training ================================ train_start = time.time() train_metrics = [] # Create sampling rng rng, input_rng = jax.random.split(rng) # Generate an epoch by shuffling sampling indices from the train dataset num_train_samples = len(tokenized_datasets["train"]) # Avoid using jax.numpy here in case of TPU training train_samples_idx = np.random.permutation(np.arange(num_train_samples)) train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size) # Gather the indexes for creating the batch and do a training step for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)): samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) cur_step = epoch * (num_train_samples // train_batch_size) + step if cur_step % training_args.logging_steps == 0 and cur_step > 0: # Save metrics train_metric = jax_utils.unreplicate(train_metric) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, cur_step) epochs.write( f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:" f" {train_metric['learning_rate']})" ) train_metrics = [] if cur_step % training_args.eval_steps == 0 and cur_step > 0: # ======================== Evaluating ============================== num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})" # Save metrics if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, cur_step) if cur_step % training_args.save_steps == 0 and cur_step > 0: # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained(training_args.output_dir, params=params) tokenizer.save_pretrained(training_args.output_dir) if training_args.push_to_hub: api.upload_folder( commit_message=f"Saving weights and logs of step {cur_step}", folder_path=training_args.output_dir, repo_id=repo_id, repo_type="model", token=training_args.hub_token, ) # Eval after training if training_args.do_eval: num_eval_samples = len(tokenized_datasets["validation"]) # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) eval_metrics = [] for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] model_inputs = data_collator(samples) # Model forward metrics = pad_shard_unpad(p_eval_step, static_return=True)( state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size ) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) try: perplexity = math.exp(eval_metrics["loss"]) except OverflowError: perplexity = float("inf") eval_metrics["perplexity"] = perplexity if jax.process_index() == 0: eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()} path = os.path.join(training_args.output_dir, "eval_results.json") with open(path, "w") as f: json.dump(eval_metrics, f, indent=4, sort_keys=True) if __name__ == "__main__": main()

transformers/examples/flax/language-modeling/run_bart_dlm_flax.py/0

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# Text classification examples ## GLUE tasks Based on the script [`run_flax_glue.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/text-classification/run_flax_glue.py). Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/). This script can fine-tune any of the models on the [hub](https://huggingface.co/models) and can also be used for a dataset hosted on our [hub](https://huggingface.co/datasets) or your own data in a csv or a JSON file (the script might need some tweaks in that case, refer to the comments inside for help). GLUE is made up of a total of 9 different tasks. Here is how to run the script on one of them: ```bash export TASK_NAME=mrpc python run_flax_glue.py \ --model_name_or_path google-bert/bert-base-cased \ --task_name ${TASK_NAME} \ --max_seq_length 128 \ --learning_rate 2e-5 \ --num_train_epochs 3 \ --per_device_train_batch_size 4 \ --eval_steps 100 \ --output_dir ./$TASK_NAME/ \ --push_to_hub ``` where task name can be one of cola, mnli, mnli_mismatched, mnli_matched, mrpc, qnli, qqp, rte, sst2, stsb, wnli. Using the command above, the script will train for 3 epochs and run eval after each epoch. Metrics and hyperparameters are stored in Tensorflow event files in `--output_dir`. You can see the results by running `tensorboard` in that directory: ```bash $ tensorboard --logdir . ``` or directly on the hub under *Training metrics*. ### Accuracy Evaluation We train five replicas and report mean accuracy and stdev on the dev set below. We use the settings as in the command above (with an exception for MRPC and WNLI which are tiny and where we used 5 epochs instead of 3), and we use a total train batch size of 32 (we train on 8 Cloud v3 TPUs, so a per-device batch size of 4), On the task other than MRPC and WNLI we train for 3 these epochs because this is the standard, but looking at the training curves of some of them (e.g., SST-2, STS-b), it appears the models are undertrained and we could get better results when training longer. In the Tensorboard results linked below, the random seed of each model is equal to the ID of the run. So in order to reproduce run 1, run the command above with `--seed=1`. The best run used random seed 3, which is the default in the script. The results of all runs are in [this Google Sheet](https://docs.google.com/spreadsheets/d/1p3XzReMO75m_XdEJvPue-PIq_PN-96J2IJpJW1yS-10/edit?usp=sharing). | Task | Metric | Acc (best run) | Acc (avg/5runs) | Stdev | Metrics | |-------|------------------------------|----------------|-----------------|-----------|--------------------------------------------------------------------------| | CoLA | Matthews corr | 60.57 | 59.04 | 1.06 | [tfhub.dev](https://tensorboard.dev/experiment/lfr2adVpRtmLDALKrElkzg/) | | SST-2 | Accuracy | 92.66 | 92.23 | 0.57 | [tfhub.dev](https://tensorboard.dev/experiment/jYvfv2trRHKMjoWnXVwrZA/) | | MRPC | F1/Accuracy | 89.90/85.78 | 88.97/84.36 | 0.72/1.09 | [tfhub.dev](https://tensorboard.dev/experiment/bo3W3DEoRw2Q7YXjWrJkfg/) | | STS-B | Pearson/Spearman corr. | 89.04/88.70 | 88.94/88.63 | 0.07/0.07 | [tfhub.dev](https://tensorboard.dev/experiment/fxVwbLD7QpKhbot0r9rn2w/) | | QQP | Accuracy/F1 | 90.81/87.58 | 90.76/87.51 | 0.05/0.06 | [tfhub.dev](https://tensorboard.dev/experiment/di089Rc9TZmsnKRMrYNLsA/) | | MNLI | Matched acc. | 84.10 | 83.80 | 0.16 | [tfhub.dev](https://tensorboard.dev/experiment/JgNCGHDJSRaW6HBx6YQFYQ/) | | QNLI | Accuracy | 91.01 | 90.82 | 0.17 | [tfhub.dev](https://tensorboard.dev/experiment/Bq7cMGJnQMSggYgL8qNGeQ/) | | RTE | Accuracy | 66.06 | 64.76 | 1.04 | [tfhub.dev](https://tensorboard.dev/experiment/66Eq24bhRjqN6CEhgDSGqQ/) | | WNLI | Accuracy | 46.48 | 37.01 | 6.83 | [tfhub.dev](https://tensorboard.dev/experiment/TAqcnddqTkWvVEeGaWwIdQ/) | Some of these results are significantly different from the ones reported on the test set of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the website. ### Runtime evaluation We also ran each task once on a single V100 GPU, 8 V100 GPUs, and 8 Cloud v3 TPUs and report the overall training time below. For comparison we ran Pytorch's [run_glue.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py) on a single GPU (last column). | Task | TPU v3-8 | 8 GPU | [1 GPU](https://tensorboard.dev/experiment/mkPS4Zh8TnGe1HB6Yzwj4Q) | 1 GPU (Pytorch) | |-------|-----------|------------|------------|-----------------| | CoLA | 1m 42s | 1m 26s | 3m 9s | 4m 6s | | SST-2 | 5m 12s | 6m 28s | 22m 33s | 34m 37s | | MRPC | 1m 29s | 1m 14s | 2m 20s | 2m 56s | | STS-B | 1m 30s | 1m 12s | 2m 16s | 2m 48s | | QQP | 22m 50s | 31m 48s | 1h 59m 41s | 2h 54m | | MNLI | 25m 03s | 33m 55s | 2h 9m 37s | 3h 7m 6s | | QNLI | 7m30s | 9m 40s | 34m 40s | 49m 8s | | RTE | 1m 20s | 55s | 1m 10s | 1m 16s | | WNLI | 1m 11s | 48s | 39s | 36s | |-------| | **TOTAL** | 1h 03m | 1h 28m | 5h 16m | 6h 37m | *All experiments are ran on Google Cloud Platform. GPU experiments are ran without further optimizations besides JAX transformations. GPU experiments are ran with full precision (fp32). "TPU v3-8" are 8 TPU cores on 4 chips (each chips has 2 cores), while "8 GPU" are 8 GPU chips.

transformers/examples/flax/text-classification/README.md/0

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# Sequence-to-Sequence Training and Evaluation This directory contains examples for finetuning and evaluating transformers on summarization and translation tasks. For deprecated `bertabs` instructions, see [`bertabs/README.md`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/bertabs/README.md). ### Supported Architectures - `BartForConditionalGeneration` - `MarianMTModel` - `PegasusForConditionalGeneration` - `MBartForConditionalGeneration` - `FSMTForConditionalGeneration` - `T5ForConditionalGeneration` ### Download the Datasets #### XSUM ```bash cd examples/legacy/seq2seq wget https://cdn-datasets.huggingface.co/summarization/xsum.tar.gz tar -xzvf xsum.tar.gz export XSUM_DIR=${PWD}/xsum ``` this should make a directory called `xsum/` with files like `test.source`. To use your own data, copy that files format. Each article to be summarized is on its own line. #### CNN/DailyMail ```bash cd examples/legacy/seq2seq wget https://cdn-datasets.huggingface.co/summarization/cnn_dm_v2.tgz tar -xzvf cnn_dm_v2.tgz # empty lines removed mv cnn_cln cnn_dm export CNN_DIR=${PWD}/cnn_dm ``` this should make a directory called `cnn_dm/` with 6 files. #### WMT16 English-Romanian Translation Data download with this command: ```bash wget https://cdn-datasets.huggingface.co/translation/wmt_en_ro.tar.gz tar -xzvf wmt_en_ro.tar.gz export ENRO_DIR=${PWD}/wmt_en_ro ``` this should make a directory called `wmt_en_ro/` with 6 files. #### WMT English-German ```bash wget https://cdn-datasets.huggingface.co/translation/wmt_en_de.tgz tar -xzvf wmt_en_de.tgz export DATA_DIR=${PWD}/wmt_en_de ``` #### FSMT datasets (wmt) Refer to the scripts starting with `eval_` under: https://github.com/huggingface/transformers/tree/main/scripts/fsmt #### Pegasus (multiple datasets) Multiple eval datasets are available for download from: https://github.com/stas00/porting/tree/master/datasets/pegasus #### Your Data If you are using your own data, it must be formatted as one directory with 6 files: ``` train.source train.target val.source val.target test.source test.target ``` The `.source` files are the input, the `.target` files are the desired output. ### Potential issues - native AMP (`--fp16` and no apex) may lead to a huge memory leak and require 10x gpu memory. This has been fixed in pytorch-nightly and the minimal official version to have this fix will be pytorch-1.7.1. Until then if you have to use mixed precision please use AMP only with pytorch-nightly or NVIDIA's apex. Reference: https://github.com/huggingface/transformers/issues/8403 ### Tips and Tricks General Tips: - since you need to run from `examples/legacy/seq2seq`, and likely need to modify code, the easiest workflow is fork transformers, clone your fork, and run `pip install -e .` before you get started. - try `--freeze_encoder` or `--freeze_embeds` for faster training/larger batch size. (3hr per epoch with bs=8, see the "xsum_shared_task" command below) - `fp16_opt_level=O1` (the default works best). - In addition to the pytorch-lightning .ckpt checkpoint, a transformers checkpoint will be saved. Load it with `BartForConditionalGeneration.from_pretrained(f'{output_dir}/best_tfmr)`. - At the moment, `--do_predict` does not work in a multi-gpu setting. You need to use `evaluate_checkpoint` or the `run_eval.py` code. - This warning can be safely ignored: > "Some weights of BartForConditionalGeneration were not initialized from the model checkpoint at facebook/bart-large-xsum and are newly initialized: ['final_logits_bias']" - Both finetuning and eval are 30% faster with `--fp16`. For that you need to [install apex](https://github.com/NVIDIA/apex#quick-start). - Read scripts before you run them! Summarization Tips: - (summ) 1 epoch at batch size 1 for bart-large takes 24 hours and requires 13GB GPU RAM with fp16 on an NVIDIA-V100. - If you want to run experiments on improving the summarization finetuning process, try the XSUM Shared Task (below). It's faster to train than CNNDM because the summaries are shorter. - For CNN/DailyMail, the default `val_max_target_length` and `test_max_target_length` will truncate the ground truth labels, resulting in slightly higher rouge scores. To get accurate rouge scores, you should rerun calculate_rouge on the `{output_dir}/test_generations.txt` file saved by `trainer.test()` - `--max_target_length=60 --val_max_target_length=60 --test_max_target_length=100 ` is a reasonable setting for XSUM. - `wandb` can be used by specifying `--logger_name wandb`. It is useful for reproducibility. Specify the environment variable `WANDB_PROJECT='hf_xsum'` to do the XSUM shared task. - If you are finetuning on your own dataset, start from `distilbart-cnn-12-6` if you want long summaries and `distilbart-xsum-12-6` if you want short summaries. (It rarely makes sense to start from `bart-large` unless you are a researching finetuning methods). **Update 2018-07-18** Datasets: `LegacySeq2SeqDataset` will be used for all tokenizers without a `prepare_seq2seq_batch` method. Otherwise, `Seq2SeqDataset` will be used. Future work/help wanted: A new dataset to support multilingual tasks. ### Fine-tuning using Seq2SeqTrainer To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer`-related `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument. With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set. To see all the possible command line options, run: ```bash python finetune_trainer.py --help ``` For multi-gpu training use `torch.distributed.launch`, e.g. with 2 gpus: ```bash torchrun --nproc_per_node=2 finetune_trainer.py ... ``` **At the moment, `Seq2SeqTrainer` does not support *with teacher* distillation.** All `Seq2SeqTrainer`-based fine-tuning scripts are included in the `builtin_trainer` directory. #### TPU Training `Seq2SeqTrainer` supports TPU training with few caveats 1. As `generate` method does not work on TPU at the moment, `predict_with_generate` cannot be used. You should use `--prediction_loss_only` to only calculate loss, and do not set `--do_predict` and `--predict_with_generate`. 2. All sequences should be padded to be of equal length to avoid extremely slow training. (`finetune_trainer.py` does this automatically when running on TPU.) We provide a very simple launcher script named `xla_spawn.py` that lets you run our example scripts on multiple TPU cores without any boilerplate. Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for `torch.distributed`). `builtin_trainer/finetune_tpu.sh` script provides minimal arguments needed for TPU training. The following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins. ```bash ./builtin_trainer/train_distil_marian_enro_tpu.sh ``` ## Evaluation Commands To create summaries for each article in dataset, we use `run_eval.py`, here are a few commands that run eval for different tasks and models. If 'translation' is in your task name, the computed metric will be BLEU. Otherwise, ROUGE will be used. For t5, you need to specify --task translation_{src}_to_{tgt} as follows: ```bash export DATA_DIR=wmt_en_ro ./run_eval.py google-t5/t5-base \ $DATA_DIR/val.source t5_val_generations.txt \ --reference_path $DATA_DIR/val.target \ --score_path enro_bleu.json \ --task translation_en_to_ro \ --n_obs 100 \ --device cuda \ --fp16 \ --bs 32 ``` This command works for MBART, although the BLEU score is suspiciously low. ```bash export DATA_DIR=wmt_en_ro ./run_eval.py facebook/mbart-large-en-ro $DATA_DIR/val.source mbart_val_generations.txt \ --reference_path $DATA_DIR/val.target \ --score_path enro_bleu.json \ --task translation \ --n_obs 100 \ --device cuda \ --fp16 \ --bs 32 ``` Summarization (xsum will be very similar): ```bash export DATA_DIR=cnn_dm ./run_eval.py sshleifer/distilbart-cnn-12-6 $DATA_DIR/val.source dbart_val_generations.txt \ --reference_path $DATA_DIR/val.target \ --score_path cnn_rouge.json \ --task summarization \ --n_obs 100 \ th 56 \ --fp16 \ --bs 32 ``` ### Multi-GPU Evaluation here is a command to run xsum evaluation on 8 GPUS. It is more than linearly faster than run_eval.py in some cases because it uses SortishSampler to minimize padding. You can also use it on 1 GPU. `data_dir` must have `{type_path}.source` and `{type_path}.target`. Run `./run_distributed_eval.py --help` for all clargs. ```bash torchrun --nproc_per_node=8 run_distributed_eval.py \ --model_name sshleifer/distilbart-large-xsum-12-3 \ --save_dir xsum_generations \ --data_dir xsum \ --fp16 # you can pass generate kwargs like num_beams here, just like run_eval.py ``` Contributions that implement this command for other distributed hardware setups are welcome! #### Single-GPU Eval: Tips and Tricks When using `run_eval.py`, the following features can be useful: * if you running the script multiple times and want to make it easier to track what arguments produced that output, use `--dump-args`. Along with the results it will also dump any custom params that were passed to the script. For example if you used: `--num_beams 8 --early_stopping true`, the output will be: ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True} ``` `--info` is an additional argument available for the same purpose of tracking the conditions of the experiment. It's useful to pass things that weren't in the argument list, e.g. a language pair `--info "lang:en-ru"`. But also if you pass `--info` without a value it will fallback to the current date/time string, e.g. `2020-09-13 18:44:43`. If using `--dump-args --info`, the output will be: ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True, 'info': '2020-09-13 18:44:43'} ``` If using `--dump-args --info "pair:en-ru chkpt=best`, the output will be: ```json {'bleu': 26.887, 'n_obs': 10, 'runtime': 1, 'seconds_per_sample': 0.1, 'num_beams': 8, 'early_stopping': True, 'info': 'pair=en-ru chkpt=best'} ``` * if you need to perform a parametric search in order to find the best ones that lead to the highest BLEU score, let `run_eval_search.py` to do the searching for you. The script accepts the exact same arguments as `run_eval.py`, plus an additional argument `--search`. The value of `--search` is parsed, reformatted and fed to ``run_eval.py`` as additional args. The format for the `--search` value is a simple string with hparams and colon separated values to try, e.g.: ``` --search "num_beams=5:10 length_penalty=0.8:1.0:1.2 early_stopping=true:false" ``` which will generate `12` `(2*3*2)` searches for a product of each hparam. For example the example that was just used will invoke `run_eval.py` repeatedly with: ``` --num_beams 5 --length_penalty 0.8 --early_stopping true --num_beams 5 --length_penalty 0.8 --early_stopping false [...] --num_beams 10 --length_penalty 1.2 --early_stopping false ``` On completion, this function prints a markdown table of the results sorted by the best BLEU score and the winning arguments. ``` bleu | num_beams | length_penalty | early_stopping ----- | --------- | -------------- | -------------- 26.71 | 5 | 1.1 | 1 26.66 | 5 | 0.9 | 1 26.66 | 5 | 0.9 | 0 26.41 | 5 | 1.1 | 0 21.94 | 1 | 0.9 | 1 21.94 | 1 | 0.9 | 0 21.94 | 1 | 1.1 | 1 21.94 | 1 | 1.1 | 0 Best score args: stas/wmt19-en-ru data/en-ru/val.source data/en-ru/test_translations.txt --reference_path data/en-ru/val.target --score_path data/en-ru/test_bleu.json --bs 8 --task translation --num_beams 5 --length_penalty 1.1 --early_stopping True ``` If you pass `--info "some experiment-specific info"` it will get printed before the results table - this is useful for scripting and multiple runs, so one can tell the different sets of results from each other. ### Contributing - follow the standard contributing guidelines and code of conduct. - add tests to `test_seq2seq_examples.py` - To run only the seq2seq tests, you must be in the root of the repository and run: ```bash pytest examples/seq2seq/ ``` ### Converting pytorch-lightning checkpoints pytorch lightning ``-do_predict`` often fails, after you are done training, the best way to evaluate your model is to convert it. This should be done for you, with a file called `{save_dir}/best_tfmr`. If that file doesn't exist but you have a lightning `.ckpt` file, you can run ```bash python convert_pl_checkpoint_to_hf.py PATH_TO_CKPT randomly_initialized_hf_model_path save_dir/best_tfmr ``` Then either `run_eval` or `run_distributed_eval` with `save_dir/best_tfmr` (see previous sections) # Experimental Features These features are harder to use and not always useful. ### Dynamic Batch Size for MT `finetune.py` has a command line arg `--max_tokens_per_batch` that allows batches to be dynamically sized. This feature can only be used: - with fairseq installed - on 1 GPU - without sortish sampler - after calling `./save_len_file.py $tok $data_dir` For example, ```bash ./save_len_file.py Helsinki-NLP/opus-mt-en-ro wmt_en_ro ./dynamic_bs_example.sh --max_tokens_per_batch=2000 --output_dir benchmark_dynamic_bs ``` splits `wmt_en_ro/train` into 11,197 uneven length batches and can finish 1 epoch in 8 minutes on a v100. For comparison, ```bash ./dynamic_bs_example.sh --sortish_sampler --train_batch_size 48 ``` uses 12,723 batches of length 48 and takes slightly more time 9.5 minutes. The feature is still experimental, because: + we can make it much more robust if we have memory mapped/preprocessed datasets. + The speedup over sortish sampler is not that large at the moment.

transformers/examples/legacy/seq2seq/README.md/0

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### Motivation Without processing, english-> romanian mbart-large-en-ro gets BLEU score 26.8 on the WMT data. With post processing, it can score 37.. Here is the postprocessing code, stolen from @mjpost in this [issue](https://github.com/pytorch/fairseq/issues/1758) ### Instructions Note: You need to have your test_generations.txt before you start this process. (1) Setup `mosesdecoder` and `wmt16-scripts` ```bash cd $HOME git clone [email protected]:moses-smt/mosesdecoder.git cd mosesdecoder git clone [email protected]:rsennrich/wmt16-scripts.git ``` (2) define a function for post processing. It removes diacritics and does other things I don't understand ```bash ro_post_process () { sys=$1 ref=$2 export MOSES_PATH=$HOME/mosesdecoder REPLACE_UNICODE_PUNCT=$MOSES_PATH/scripts/tokenizer/replace-unicode-punctuation.perl NORM_PUNC=$MOSES_PATH/scripts/tokenizer/normalize-punctuation.perl REM_NON_PRINT_CHAR=$MOSES_PATH/scripts/tokenizer/remove-non-printing-char.perl REMOVE_DIACRITICS=$MOSES_PATH/wmt16-scripts/preprocess/remove-diacritics.py NORMALIZE_ROMANIAN=$MOSES_PATH/wmt16-scripts/preprocess/normalise-romanian.py TOKENIZER=$MOSES_PATH/scripts/tokenizer/tokenizer.perl lang=ro for file in $sys $ref; do cat $file \ | $REPLACE_UNICODE_PUNCT \ | $NORM_PUNC -l $lang \ | $REM_NON_PRINT_CHAR \ | $NORMALIZE_ROMANIAN \ | $REMOVE_DIACRITICS \ | $TOKENIZER -no-escape -l $lang \ > $(basename $file).tok done # compute BLEU cat $(basename $sys).tok | sacrebleu -tok none -s none -b $(basename $ref).tok } ``` (3) Call the function on test_generations.txt and test.target For example, ```bash ro_post_process enro_finetune/test_generations.txt wmt_en_ro/test.target ``` This will split out a new blue score and write a new fine called `test_generations.tok` with post-processed outputs. ```

transformers/examples/legacy/seq2seq/romanian_postprocessing.md/0

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import logging import os from typing import List, TextIO, Union from conllu import parse_incr from utils_ner import InputExample, Split, TokenClassificationTask logger = logging.getLogger(__name__) class NER(TokenClassificationTask): def __init__(self, label_idx=-1): # in NER datasets, the last column is usually reserved for NER label self.label_idx = label_idx def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 words = [] labels = [] else: splits = line.split(" ") words.append(splits[0]) if len(splits) > 1: labels.append(splits[self.label_idx].replace("\n", "")) else: # Examples could have no label for mode = "test" labels.append("O") if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for line in test_input_reader: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not preds_list[example_id]: example_id += 1 elif preds_list[example_id]: output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n" writer.write(output_line) else: logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] class Chunk(NER): def __init__(self): # in CONLL2003 dataset chunk column is second-to-last super().__init__(label_idx=-2) def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: labels = f.read().splitlines() if "O" not in labels: labels = ["O"] + labels return labels else: return [ "O", "B-ADVP", "B-INTJ", "B-LST", "B-PRT", "B-NP", "B-SBAR", "B-VP", "B-ADJP", "B-CONJP", "B-PP", "I-ADVP", "I-INTJ", "I-LST", "I-PRT", "I-NP", "I-SBAR", "I-VP", "I-ADJP", "I-CONJP", "I-PP", ] class POS(TokenClassificationTask): def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: for sentence in parse_incr(f): words = [] labels = [] for token in sentence: words.append(token["form"]) labels.append(token["upos"]) assert len(words) == len(labels) if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 return examples def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List): example_id = 0 for sentence in parse_incr(test_input_reader): s_p = preds_list[example_id] out = "" for token in sentence: out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0)}) ' out += "\n" writer.write(out) example_id += 1 def get_labels(self, path: str) -> List[str]: if path: with open(path, "r") as f: return f.read().splitlines() else: return [ "ADJ", "ADP", "ADV", "AUX", "CCONJ", "DET", "INTJ", "NOUN", "NUM", "PART", "PRON", "PROPN", "PUNCT", "SCONJ", "SYM", "VERB", "X", ]

transformers/examples/legacy/token-classification/tasks.py/0

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#!/usr/bin/env python # coding=utf-8 # Copyright The HuggingFace Team 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. """ Fine-tuning the library models for multiple choice. """ # You can also adapt this script on your own multiple choice task. Pointers for this are left as comments. import logging import os import sys import warnings from dataclasses import dataclass, field from itertools import chain from typing import Optional, Union import datasets import numpy as np import torch from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.tokenization_utils_base import PreTrainedTokenizerBase from transformers.trainer_utils import get_last_checkpoint from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.40.0.dev0") logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) token: str = field( default=None, metadata={ "help": ( "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " "generated when running `huggingface-cli login` (stored in `~/.huggingface`)." ) }, ) use_auth_token: bool = field( default=None, metadata={ "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead." }, ) trust_remote_code: bool = field( default=False, metadata={ "help": ( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. If passed, sequences longer " "than this will be truncated, sequences shorter will be padded." ) }, ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to the maximum sentence length. " "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " "efficient on GPU but very bad for TPU." ) }, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) def __post_init__(self): if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." @dataclass class DataCollatorForMultipleChoice: """ Data collator that will dynamically pad the inputs for multiple choice received. Args: tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]): The tokenizer used for encoding the data. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): 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). pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ tokenizer: PreTrainedTokenizerBase padding: Union[bool, str, PaddingStrategy] = True max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None def __call__(self, features): label_name = "label" if "label" in features[0].keys() else "labels" labels = [feature.pop(label_name) for feature in features] batch_size = len(features) num_choices = len(features[0]["input_ids"]) flattened_features = [ [{k: v[i] for k, v in feature.items()} for i in range(num_choices)] for feature in features ] flattened_features = list(chain(*flattened_features)) batch = self.tokenizer.pad( flattened_features, padding=self.padding, max_length=self.max_length, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) # Un-flatten batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()} # Add back labels batch["labels"] = torch.tensor(labels, dtype=torch.int64) return batch def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if model_args.use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.", FutureWarning, ) if model_args.token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") model_args.token = model_args.use_auth_token # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_swag", model_args, data_args) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, " + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.train_file is not None or data_args.validation_file is not None: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] raw_datasets = load_dataset( extension, data_files=data_files, cache_dir=model_args.cache_dir, token=model_args.token, ) else: # Downloading and loading the swag dataset from the hub. raw_datasets = load_dataset( "swag", "regular", cache_dir=model_args.cache_dir, token=model_args.token, ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets. # Load pretrained model and tokenizer # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) model = AutoModelForMultipleChoice.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=model_args.token, trust_remote_code=model_args.trust_remote_code, ) # When using your own dataset or a different dataset from swag, you will probably need to change this. ending_names = [f"ending{i}" for i in range(4)] context_name = "sent1" question_header_name = "sent2" if data_args.max_seq_length is None: max_seq_length = tokenizer.model_max_length if max_seq_length > 1024: logger.warning( "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value" " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can" " override this default with `--block_size xxx`." ) max_seq_length = 1024 else: if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the " f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) # Preprocessing the datasets. def preprocess_function(examples): first_sentences = [[context] * 4 for context in examples[context_name]] question_headers = examples[question_header_name] second_sentences = [ [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers) ] # Flatten out first_sentences = list(chain(*first_sentences)) second_sentences = list(chain(*second_sentences)) # Tokenize tokenized_examples = tokenizer( first_sentences, second_sentences, truncation=True, max_length=max_seq_length, padding="max_length" if data_args.pad_to_max_length else False, ) # Un-flatten return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()} if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if data_args.max_train_samples is not None: max_train_samples = min(len(train_dataset), data_args.max_train_samples) train_dataset = train_dataset.select(range(max_train_samples)) with training_args.main_process_first(desc="train dataset map pre-processing"): train_dataset = train_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, ) if training_args.do_eval: if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_dataset = raw_datasets["validation"] if data_args.max_eval_samples is not None: max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) eval_dataset = eval_dataset.select(range(max_eval_samples)) with training_args.main_process_first(desc="validation dataset map pre-processing"): eval_dataset = eval_dataset.map( preprocess_function, batched=True, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, ) # Data collator data_collator = ( default_data_collator if data_args.pad_to_max_length else DataCollatorForMultipleChoice(tokenizer=tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None) ) # Metric def compute_metrics(eval_predictions): predictions, label_ids = eval_predictions preds = np.argmax(predictions, axis=1) return {"accuracy": (preds == label_ids).astype(np.float32).mean().item()} # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) # Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) ) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "multiple-choice", "dataset_tags": "swag", "dataset_args": "regular", "dataset": "SWAG", "language": "en", } if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

transformers/examples/pytorch/multiple-choice/run_swag.py/0

{ "file_path": "transformers/examples/pytorch/multiple-choice/run_swag.py", "repo_id": "transformers", "token_count": 8510 }

259

# coding=utf-8 # Copyright 2022 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. """ Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation.""" import argparse import json import math import os import random from pathlib import Path import datasets import evaluate import numpy as np import torch from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from datasets import load_dataset from huggingface_hub import HfApi, hf_hub_download from PIL import Image from torch.utils.data import DataLoader from torchvision import transforms from torchvision.transforms import functional from tqdm.auto import tqdm import transformers from transformers import ( AutoConfig, AutoImageProcessor, AutoModelForSemanticSegmentation, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.40.0.dev0") logger = get_logger(__name__) require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt") def pad_if_smaller(img, size, fill=0): min_size = min(img.size) if min_size < size: original_width, original_height = img.size pad_height = size - original_height if original_height < size else 0 pad_width = size - original_width if original_width < size else 0 img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill) return img class Compose: def __init__(self, transforms): self.transforms = transforms def __call__(self, image, target): for t in self.transforms: image, target = t(image, target) return image, target class Identity: def __init__(self): pass def __call__(self, image, target): return image, target class Resize: def __init__(self, size): self.size = size def __call__(self, image, target): image = functional.resize(image, self.size) target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomResize: def __init__(self, min_size, max_size=None): self.min_size = min_size if max_size is None: max_size = min_size self.max_size = max_size def __call__(self, image, target): size = random.randint(self.min_size, self.max_size) image = functional.resize(image, size) target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST) return image, target class RandomCrop: def __init__(self, size): self.size = size def __call__(self, image, target): image = pad_if_smaller(image, self.size) target = pad_if_smaller(target, self.size, fill=255) crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size)) image = functional.crop(image, *crop_params) target = functional.crop(target, *crop_params) return image, target class RandomHorizontalFlip: def __init__(self, flip_prob): self.flip_prob = flip_prob def __call__(self, image, target): if random.random() < self.flip_prob: image = functional.hflip(image) target = functional.hflip(target) return image, target class PILToTensor: def __call__(self, image, target): image = functional.pil_to_tensor(image) target = torch.as_tensor(np.array(target), dtype=torch.int64) return image, target class ConvertImageDtype: def __init__(self, dtype): self.dtype = dtype def __call__(self, image, target): image = functional.convert_image_dtype(image, self.dtype) return image, target class Normalize: def __init__(self, mean, std): self.mean = mean self.std = std def __call__(self, image, target): image = functional.normalize(image, mean=self.mean, std=self.std) return image, target class ReduceLabels: def __call__(self, image, target): if not isinstance(target, np.ndarray): target = np.array(target).astype(np.uint8) # avoid using underflow conversion target[target == 0] = 255 target = target - 1 target[target == 254] = 255 target = Image.fromarray(target) return image, target def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task") parser.add_argument( "--model_name_or_path", type=str, help="Path to a pretrained model or model identifier from huggingface.co/models.", default="nvidia/mit-b0", ) parser.add_argument( "--dataset_name", type=str, help="Name of the dataset on the hub.", default="segments/sidewalk-semantic", ) parser.add_argument( "--reduce_labels", action="store_true", help="Whether or not to reduce all labels by 1 and replace background by 255.", ) parser.add_argument( "--train_val_split", type=float, default=0.15, help="Fraction of the dataset to be used for validation.", ) parser.add_argument( "--cache_dir", type=str, help="Path to a folder in which the model and dataset will be cached.", ) parser.add_argument( "--use_auth_token", action="store_true", help="Whether to use an authentication token to access the model repository.", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="Beta1 for AdamW optimizer", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="Beta2 for AdamW optimizer", ) parser.add_argument( "--adam_epsilon", type=float, default=1e-8, help="Epsilon for AdamW optimizer", ) parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="polynomial", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--trust_remote_code", type=bool, default=False, help=( "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option " "should only be set to `True` for repositories you trust and in which you have read the code, as it will " "execute code present on the Hub on your local machine." ), ) parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", required=False, action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. ' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if args.push_to_hub or args.with_tracking: if args.output_dir is None: raise ValueError( "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified." ) if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_semantic_segmentation_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["project_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. # We set device_specific to True as we want different data augmentation per device. if args.seed is not None: set_seed(args.seed, device_specific=True) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: # Retrieve of infer repo_name repo_name = args.hub_model_id if repo_name is None: repo_name = Path(args.output_dir).absolute().name # Create repo and retrieve repo_id api = HfApi() repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Load dataset # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # TODO support datasets from local folders dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir) # Rename column names to standardized names (only "image" and "label" need to be present) if "pixel_values" in dataset["train"].column_names: dataset = dataset.rename_columns({"pixel_values": "image"}) if "annotation" in dataset["train"].column_names: dataset = dataset.rename_columns({"annotation": "label"}) # If we don't have a validation split, split off a percentage of train as validation. args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split if isinstance(args.train_val_split, float) and args.train_val_split > 0.0: split = dataset["train"].train_test_split(args.train_val_split) dataset["train"] = split["train"] dataset["validation"] = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. if args.dataset_name == "scene_parse_150": repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" else: repo_id = args.dataset_name filename = "id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} # Load pretrained model and image processor config = AutoConfig.from_pretrained( args.model_name_or_path, id2label=id2label, label2id=label2id, trust_remote_code=args.trust_remote_code ) image_processor = AutoImageProcessor.from_pretrained( args.model_name_or_path, trust_remote_code=args.trust_remote_code ) model = AutoModelForSemanticSegmentation.from_pretrained( args.model_name_or_path, config=config, trust_remote_code=args.trust_remote_code ) # Preprocessing the datasets # Define torchvision transforms to be applied to each image + target. # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py if "shortest_edge" in image_processor.size: # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable. size = (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"]) else: size = (image_processor.size["height"], image_processor.size["width"]) train_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), RandomCrop(size=size), RandomHorizontalFlip(flip_prob=0.5), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=image_processor.image_mean, std=image_processor.image_std), ] ) # Define torchvision transform to be applied to each image. # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1) val_transforms = Compose( [ ReduceLabels() if args.reduce_labels else Identity(), Resize(size=size), PILToTensor(), ConvertImageDtype(torch.float), Normalize(mean=image_processor.image_mean, std=image_processor.image_std), ] ) def preprocess_train(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = train_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = {} encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding def preprocess_val(example_batch): pixel_values = [] labels = [] for image, target in zip(example_batch["image"], example_batch["label"]): image, target = val_transforms(image.convert("RGB"), target) pixel_values.append(image) labels.append(target) encoding = {} encoding["pixel_values"] = torch.stack(pixel_values) encoding["labels"] = torch.stack(labels) return encoding with accelerator.main_process_first(): train_dataset = dataset["train"].with_transform(preprocess_train) eval_dataset = dataset["validation"].with_transform(preprocess_val) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size ) eval_dataloader = DataLoader( eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size ) # Optimizer optimizer = torch.optim.AdamW( list(model.parameters()), lr=args.learning_rate, betas=[args.adam_beta1, args.adam_beta2], eps=args.adam_epsilon, ) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * accelerator.num_processes, num_training_steps=args.max_train_steps if overrode_max_train_steps else args.max_train_steps * accelerator.num_processes, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Instantiate metric metric = evaluate.load("mean_iou", cache_dir=args.cache_dir) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("semantic_segmentation_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": checkpoint_path = args.resume_from_checkpoint path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last checkpoint_path = path path = os.path.basename(checkpoint_path) accelerator.print(f"Resumed from checkpoint: {checkpoint_path}") accelerator.load_state(checkpoint_path) # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None completed_steps = starting_epoch * num_update_steps_per_epoch else: # need to multiply `gradient_accumulation_steps` to reflect real steps resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps starting_epoch = resume_step // len(train_dataloader) completed_steps = resume_step // args.gradient_accumulation_steps resume_step -= starting_epoch * len(train_dataloader) # update the progress_bar if load from checkpoint progress_bar.update(completed_steps) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None: # We skip the first `n` batches in the dataloader when resuming from a checkpoint active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step) else: active_dataloader = train_dataloader for step, batch in enumerate(active_dataloader): with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, ) if accelerator.is_main_process: image_processor.save_pretrained(args.output_dir) api.upload_folder( commit_message=f"Training in progress epoch {epoch}", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) if completed_steps >= args.max_train_steps: break logger.info("***** Running evaluation *****") model.eval() for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)): with torch.no_grad(): outputs = model(**batch) upsampled_logits = torch.nn.functional.interpolate( outputs.logits, size=batch["labels"].shape[-2:], mode="bilinear", align_corners=False ) predictions = upsampled_logits.argmax(dim=1) predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"])) metric.add_batch( predictions=predictions, references=references, ) eval_metrics = metric.compute( num_labels=len(id2label), ignore_index=255, reduce_labels=False, # we've already reduced the labels before ) logger.info(f"epoch {epoch}: {eval_metrics}") if args.with_tracking: accelerator.log( { "mean_iou": eval_metrics["mean_iou"], "mean_accuracy": eval_metrics["mean_accuracy"], "overall_accuracy": eval_metrics["overall_accuracy"], "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, }, step=completed_steps, ) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: image_processor.save_pretrained(args.output_dir) api.upload_folder( commit_message=f"Training in progress epoch {epoch}", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.with_tracking: accelerator.end_training() if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: image_processor.save_pretrained(args.output_dir) if args.push_to_hub: api.upload_folder( commit_message="End of training", folder_path=args.output_dir, repo_id=repo_id, repo_type="model", token=args.hub_token, ) all_results = { f"eval_{k}": v.tolist() if isinstance(v, np.ndarray) else v for k, v in eval_metrics.items() } with open(os.path.join(args.output_dir, "all_results.json"), "w") as f: json.dump(all_results, f) if __name__ == "__main__": main()

transformers/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py/0

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# Text Summarization with Pretrained Encoders This folder contains part of the code necessary to reproduce the results on abstractive summarization from the article [Text Summarization with Pretrained Encoders](https://arxiv.org/pdf/1908.08345.pdf) by [Yang Liu](https://nlp-yang.github.io/) and [Mirella Lapata](https://homepages.inf.ed.ac.uk/mlap/). It can also be used to summarize any document. The original code can be found on the Yang Liu's [github repository](https://github.com/nlpyang/PreSumm). The model is loaded with the pre-trained weights for the abstractive summarization model trained on the CNN/Daily Mail dataset with an extractive and then abstractive tasks. ## Setup ```bash git clone https://github.com/huggingface/transformers && cd transformers pip install . pip install nltk py-rouge cd examples/seq2seq/bertabs ``` ## Reproduce the authors' ROUGE score To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running: ```bash tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz ``` And move all the stories to the same folder. We will refer as `$DATA_PATH` the path to where you uncompressed both archive. Then run the following in the same folder as `run_summarization.py`: ```bash python run_summarization.py \ --documents_dir $DATA_PATH \ --summaries_output_dir $SUMMARIES_PATH \ # optional --no_cuda false \ --batch_size 4 \ --min_length 50 \ --max_length 200 \ --beam_size 5 \ --alpha 0.95 \ --block_trigram true \ --compute_rouge true ``` The scripts executes on GPU if one is available and if `no_cuda` is not set to `true`. Inference on multiple GPUs is not supported yet. The ROUGE scores will be displayed in the console at the end of evaluation and written in a `rouge_scores.txt` file. The script takes 30 hours to compute with a single Tesla V100 GPU and a batch size of 10 (300,000 texts to summarize). ## Summarize any text Put the documents that you would like to summarize in a folder (the path to which is referred to as `$DATA_PATH` below) and run the following in the same folder as `run_summarization.py`: ```bash python run_summarization.py \ --documents_dir $DATA_PATH \ --summaries_output_dir $SUMMARIES_PATH \ # optional --no_cuda false \ --batch_size 4 \ --min_length 50 \ --max_length 200 \ --beam_size 5 \ --alpha 0.95 \ --block_trigram true \ ``` You may want to play around with `min_length`, `max_length` and `alpha` to suit your use case. If you want to compute ROUGE on another dataset you will need to tweak the stories/summaries import in `utils_summarization.py` and tell it where to fetch the reference summaries.

transformers/examples/research_projects/bertabs/README.md/0

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#!/bin/bash export CUDA_VISIBLE_DEVICES=0 PATH_TO_DATA=/h/xinji/projects/GLUE MODEL_TYPE=bert # bert or roberta MODEL_SIZE=base # base or large DATASET=MRPC # SST-2, MRPC, RTE, QNLI, QQP, or MNLI MODEL_NAME=${MODEL_TYPE}-${MODEL_SIZE} if [ $MODEL_TYPE = 'bert' ] then MODEL_NAME=${MODEL_NAME}-uncased fi python -u run_glue_deebert.py \ --model_type $MODEL_TYPE \ --model_name_or_path ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \ --task_name $DATASET \ --do_eval \ --do_lower_case \ --data_dir $PATH_TO_DATA/$DATASET \ --output_dir ./saved_models/${MODEL_TYPE}-${MODEL_SIZE}/$DATASET/two_stage \ --plot_data_dir ./results/ \ --max_seq_length 128 \ --eval_each_highway \ --eval_highway \ --overwrite_cache \ --per_gpu_eval_batch_size=1

transformers/examples/research_projects/deebert/eval_deebert.sh/0

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# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocessing script before training DistilBERT. Specific to BERT -> DistilBERT. """ import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": parser = argparse.ArgumentParser( description=( "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned" " Distillation" ) ) parser.add_argument("--model_type", default="bert", choices=["bert"]) parser.add_argument("--model_name", default="bert-base-uncased", type=str) parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str) parser.add_argument("--vocab_transform", action="store_true") args = parser.parse_args() if args.model_type == "bert": model = BertForMaskedLM.from_pretrained(args.model_name) prefix = "bert" else: raise ValueError('args.model_type should be "bert".') state_dict = model.state_dict() compressed_sd = {} for w in ["word_embeddings", "position_embeddings"]: compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] std_idx = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"] compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"] if args.vocab_transform: for w in ["weight", "bias"]: compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"] compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

transformers/examples/research_projects/distillation/scripts/extract_distilbert.py/0

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# Information Gain Filtration(IGF) Authors @Tuko @mraunak This folder contains the code how to implement IGF for finetuning on GPT-2. ## What is IGF? Here we present a general fine-tuning method that we call information gain filtration for improving the overall training efficiency and final performance of language model fine-tuning(see paper below). The method is an alternative fine-tuning method that trains a secondary model (e.g., a simple convolutional network) to predict the amount of information gained over a given pre-trained model. The secondary model is lightweight and trained to predict the Information Gain measure. Information Gain is defined as the change in a loss function for a model before and after an SGD update with a sample (Equation X in the paper). A small subset of the training set named the “objective” set, is used to measure information gain on the pre-trained model, and consequently to train the secondary model. After training, the model is used for filtering samples for the fine-tuning process. Therefore, a high information gain value would suggest a sample is informative, whereas a low value would suggest a non-informative sample that should be filtered out. Thus, a thresholding strategy is defined to select informative samples. With such a strategy, samples are filtered and once enough samples are selected to form a mini-batch and a usual fine-tuning/optimization step is applied. The filtration process is repeated until the fine-tuning process is over. Paper [Selecting Informative Contexts Improves Language Model Finetuning](https://arxiv.org/abs/2005.00175) # Results Several experiments were conducted to show the robustness of the IGF method versus the standard fine-tuning process. For example, we achieve a median perplexity of 54.0 on the Books dataset compared to 57.3 for standard fine-tuning on GPT-2 Small. The code was implemented using the Transformers library and Pytorch. While the method may seem more expensive, we saw enough evidence that it may lead to a performance benefit in the final models. ![IGF performance](result_igf.png) Figure 1: Comparing IGF to Standard Fine-tuning: IGF with constant (p < 10−3 , t-test) and shifting(p < 10−6 , t-test) thresholding significantly outperform standard fine-tuning. The left-hand figure shows test-set perplexity after each fine-tuning batch, averaged over 50 runs (error bars denote ± one standard error). The right-hand figure shows the perplexity of each method after 60 batches. IGF with shifting thresholding (red) clearly improves over standard batched fine-tuning with Adam ## How to use this project? To fine-tune a transformer model with IGF on a language modeling task, use the following script: - `model_name_or_path`: Path to pretrained model or model identifier from huggingface.co/models - `data_file`: A jbl file containing tokenized data which can be split as objective dataset, train_dataset and test_dataset - `igf_data_file`: A jbl file containing the context and information gain pairs to train secondary learner. - `context_len`: The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded. - `size_objective_set`: Number of articles that are long enough to be used as our objective set" - `min_len`: The minimum length of the article to be used as objective set - `trim`: Truncate the example if it exceeds context length - `eval_freq`: Secondary model evaluation can be triggered at eval_freq - `max_steps`: To calculate training epochs - `number`: The number of examples split to be used as objective_set/test_data - `secondary_learner_batch_size`: The batch size of training data for secondary learner - `secondary_learner_max_epochs`: The number of epochs to train secondary learner - `recopy_model`: Reset the model to the original pretrained GPT-2 weights after each iteration - `eval_interval`: Decay the selectivity of our secondary learner filter from" 1 standard deviation above average to 1 below average after eval_interval(10) batches" ```python python run_clm_igf.py\ --model_name_or_path "openai-community/gpt2" \ --data_file="data/tokenized_stories_train_wikitext103" \ --igf_data_file="data/IGF_values" \ --context_len 32 \ --size_objective_set 100 \ --min_len 1026 \ --trim True \ --eval_freq 100 \ --max_steps 1000 \ --secondary_learner_batch_size 128 \ --secondary_learner_max_epochs 15 \ --number 100 \ --recopy_model \ --eval_interval 10 \ ``` ## Citation If you find the resource useful, please cite the following paper ```bibtex @inproceedings{antonello-etal-2021-selecting, title = "Selecting Informative Contexts Improves Language Model Fine-tuning", author = "Antonello, Richard and Beckage, Nicole and Turek, Javier and Huth, Alexander", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-long.87", doi = "10.18653/v1/2021.acl-long.87", pages = "1072--1085", } ```

transformers/examples/research_projects/information-gain-filtration/README.md/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2021 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. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ import logging import os import sys import time from collections import defaultdict from dataclasses import dataclass, field # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. from pathlib import Path from typing import Dict, List, Optional, Tuple import datasets import flax import jax import jax.numpy as jnp import numpy as np import optax from datasets import load_dataset from flax import jax_utils, traverse_util from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from tqdm import tqdm from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed, ) if datasets.__version__ <= "1.8.0": raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming") MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the column to retrieve the training text."} ) shuffle_buffer_size: int = field( default=10000, metadata={"help": "The number of examples to pre-load for shuffling."} ) num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."}) num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"}) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForLanguageModeling: """ Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length. Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. mlm_probability (:obj:`float`, `optional`, defaults to 0.15): The probability with which to (randomly) mask tokens in the input. .. note:: For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the argument :obj:`return_special_tokens_mask=True`. """ tokenizer: PreTrainedTokenizerBase mlm_probability: float = 0.15 def __post_init__(self): if self.tokenizer.mask_token is None: raise ValueError( "This tokenizer does not have a mask token which is necessary for masked language modeling. " "You should pass `mlm=False` to train on causal language modeling instead." ) def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]: # Handle dict or lists with proper padding and conversion to tensor. batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY) # If special token mask has been preprocessed, pop it from the dict. special_tokens_mask = batch.pop("special_tokens_mask", None) batch["input_ids"], batch["labels"] = self.mask_tokens( batch["input_ids"], special_tokens_mask=special_tokens_mask ) return batch def mask_tokens( self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] ) -> Tuple[jnp.ndarray, jnp.ndarray]: """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ labels = inputs.copy() # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) probability_matrix = np.full(labels.shape, self.mlm_probability) special_tokens_mask = special_tokens_mask.astype("bool") probability_matrix[special_tokens_mask] = 0.0 masked_indices = np.random.binomial(1, probability_matrix).astype("bool") labels[~masked_indices] = -100 # We only compute loss on masked tokens # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # 10% of the time, we replace masked input tokens with random word indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") indices_random &= masked_indices & ~indices_replaced random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") inputs[indices_random] = random_words[indices_random] # The rest of the time (10% of the time) we keep the masked input tokens unchanged return inputs, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray: num_samples = len(samples_idx) samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size batch_idx = np.split(samples_idx, sections_split) return batch_idx def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length): """ The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. """ num_total_tokens = max_seq_length * num_samples samples = defaultdict(list) i = 0 while i < num_total_tokens: tokenized_samples = next(train_iterator) i += len(tokenized_samples["input_ids"]) # concatenate tokenized samples to list (excluding "id" and "text") samples = { k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"] } # Concatenated tokens are split to lists of length `max_seq_length`. # Note that remainedr of % max_seq_length are thrown away. def group_texts(examples): result = { k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)] for k, t in examples.items() } return result grouped_samples = group_texts(samples) return grouped_samples def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) if __name__ == "__main__": # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level="INFO", datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=True, split="train", ) if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script. " "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys())) shuffle_seed = training_args.seed tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed) has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) # Data collator # This one will take care of randomly masking the tokens. data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxAutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxAutoModelForMaskedLM.from_config( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() # define number steps per stream epoch num_train_steps = data_args.num_train_steps # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBERT-like models. # For other models, one should correct the layer norm parameter naming # accordingly. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() / label_mask.sum() return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean( {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch" ) return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 train_start = time.time() train_metrics = [] eval_metrics = [] training_iter = iter(tokenized_datasets) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) steps = tqdm(range(num_train_steps), desc="Training...", position=0) for step in range(num_train_steps): # ======================== Training ================================ try: samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) except StopIteration: # Once the end of the dataset stream is reached, the training iterator # is reinitialized and reshuffled and a new eval dataset is randomly chosen. shuffle_seed += 1 tokenized_datasets.set_epoch(shuffle_seed) training_iter = iter(tokenized_datasets) eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) # process input samples model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) if step % training_args.logging_steps == 0 and step > 0: steps.write( f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:" f" {train_metric['learning_rate'].mean()})" ) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, step) train_metrics = [] # ======================== Evaluating ============================== if step % training_args.eval_steps == 0 and step > 0: # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(data_args.num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)): # process input samples batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()} model_inputs = data_collator(batch_eval_samples) # Model forward model_inputs = shard(model_inputs.data) metrics = p_eval_step(state.params, model_inputs) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar steps.desc = ( f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:" f" {eval_metrics['accuracy']})" ) if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, step) eval_metrics = [] # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained( training_args.output_dir, params=params, push_to_hub=training_args.push_to_hub, commit_message=f"Saving weights and logs of step {step+1}", ) # update tqdm bar steps.update(1)

transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py/0

{ "file_path": "transformers/examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py", "repo_id": "transformers", "token_count": 10617 }

265

# coding=utf-8 # Copyright 2020 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. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. import json import logging import math import os import sys from dataclasses import dataclass, field from typing import Optional from datasets import Dataset, load_dataset import transformers from transformers import ( CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForWholeWordMask, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_overrides: Optional[str] = field( default=None, metadata={ "help": ( "Override some existing default config settings when a model is trained from scratch. Example: " "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" ) }, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) def __post_init__(self): if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): raise ValueError( "--config_overrides can't be used in combination with --config_name or --model_name_or_path" ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) def __post_init__(self): if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." def add_chinese_references(dataset, ref_file): with open(ref_file, "r", encoding="utf-8") as f: refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] assert len(dataset) == len(refs) dataset_dict = {c: dataset[c] for c in dataset.column_names} dataset_dict["chinese_ref"] = refs return Dataset.from_dict(dataset_dict) def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) if "validation" not in datasets.keys(): datasets["validation"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[:{data_args.validation_split_percentage}%]", ) datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=f"train[{data_args.validation_split_percentage}%:]", ) else: data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file extension = data_args.train_file.split(".")[-1] if data_args.validation_file is not None: data_files["validation"] = data_args.validation_file extension = data_args.validation_file.split(".")[-1] if extension == "txt": extension = "text" datasets = load_dataset(extension, data_files=data_files) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets. # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. config_kwargs = { "cache_dir": model_args.cache_dir, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.config_overrides is not None: logger.info(f"Overriding config: {model_args.config_overrides}") config.update_from_string(model_args.config_overrides) logger.info(f"New config: {config}") tokenizer_kwargs = { "cache_dir": model_args.cache_dir, "use_fast": model_args.use_fast_tokenizer, "revision": model_args.model_revision, "use_auth_token": True if model_args.use_auth_token else None, } if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script. " "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if model_args.model_name_or_path: model = AutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, token=True if model_args.use_auth_token else None, ) else: logger.info("Training new model from scratch") model = AutoModelForMaskedLM.from_config(config) model.resize_token_embeddings(len(tokenizer)) # Preprocessing the datasets. # First we tokenize all the texts. if training_args.do_train: column_names = datasets["train"].column_names else: column_names = datasets["validation"].column_names text_column_name = "text" if "text" in column_names else column_names[0] padding = "max_length" if data_args.pad_to_max_length else False def tokenize_function(examples): # Remove empty lines examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) tokenized_datasets = datasets.map( tokenize_function, batched=True, num_proc=data_args.preprocessing_num_workers, remove_columns=[text_column_name], load_from_cache_file=not data_args.overwrite_cache, ) # Add the chinese references if provided if data_args.train_ref_file is not None: tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file) if data_args.validation_ref_file is not None: tokenized_datasets["validation"] = add_chinese_references( tokenized_datasets["validation"], data_args.validation_ref_file ) # If we have ref files, need to avoid it removed by trainer has_ref = data_args.train_ref_file or data_args.validation_ref_file if has_ref: training_args.remove_unused_columns = False # Data collator # This one will take care of randomly masking the tokens. data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"] if training_args.do_train else None, eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None, tokenizer=tokenizer, data_collator=data_collator, ) # Training if training_args.do_train: if last_checkpoint is not None: checkpoint = last_checkpoint elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload output_train_file = os.path.join(training_args.output_dir, "train_results.txt") if trainer.is_world_process_zero(): with open(output_train_file, "w") as writer: logger.info("***** Train results *****") for key, value in sorted(train_result.metrics.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") # Need to save the state, since Trainer.save_model saves only the tokenizer with the model trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json")) # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") eval_output = trainer.evaluate() perplexity = math.exp(eval_output["eval_loss"]) results["perplexity"] = perplexity output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt") if trainer.is_world_process_zero(): with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key, value in sorted(results.items()): logger.info(f" {key} = {value}") writer.write(f"{key} = {value}\n") return results def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

transformers/examples/research_projects/mlm_wwm/run_mlm_wwm.py/0

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# Sample script to finetune RAG using Ray for distributed retrieval. # Add parent directory to python path to access lightning_base.py export PYTHONPATH="../":"${PYTHONPATH}" #creates the custom knowlegebase python use_own_knowledge_dataset.py \ --csv_path /DIR/SQUAD-KB/squad-kb.csv \ --output_dir /DIR/SQUAD-KB # Start a single-node Ray cluster. ray start --head # A sample finetuning run, you need to specify data_dir, output_dir and model_name_or_path # run ./examples/rag/finetune_rag_ray.sh --help to see all the possible options python finetune_rag.py \ --data_dir /DIR/squad-training-data \ --output_dir /DIR/model_checkpoints \ --model_name_or_path facebook/rag-token-base \ --model_type rag_token \ --fp16 \ --gpus 2 \ --profile \ --do_train \ --end2end \ --do_predict \ --n_val -1 \ --train_batch_size 4 \ --eval_batch_size 1 \ --max_source_length 128 \ --max_target_length 25 \ --val_max_target_length 25 \ --test_max_target_length 25 \ --label_smoothing 0.1 \ --dropout 0.1 \ --attention_dropout 0.1 \ --weight_decay 0.001 \ --adam_epsilon 1e-08 \ --max_grad_norm 0.1 \ --lr_scheduler polynomial \ --learning_rate 3e-05 \ --num_train_epochs 10 \ --warmup_steps 500 \ --gradient_accumulation_steps 8 \ --distributed_retriever ray \ --num_retrieval_workers 4 \ --passages_path /DIR/SQUAD-KB/my_knowledge_dataset \ --index_path /DIR/SQUAD-KB/my_knowledge_dataset_hnsw_index.faiss \ --index_name custom \ --context_encoder_name facebook/dpr-ctx_encoder-multiset-base \ --csv_path /DIR/SQUAD-KB/squad-kb.csv \ --index_gpus 1 \ --gpu_order [5,6,7,8,9,0,1,2,3,4] \ --shard_dir ./test_dir/kb-shards \ --indexing_freq 500 # Stop the Ray cluster. ray stop #this script was used to test the SQuAD data. #change the dir paramater acording to your prefernece. #please use the same device ordere when running CUDA_VISIBLE_DEVICES=5,6,7,8,9,0,1,2,3,4 sh finetune_rag_ray_end2end.sh

transformers/examples/research_projects/rag-end2end-retriever/finetune_rag_ray_end2end.sh/0

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#!/usr/bin/env python import os from pathlib import Path from typing import Dict, List import fire import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers.utils.logging import get_logger logger = get_logger(__name__) def remove_prefix(text: str, prefix: str): if text.startswith(prefix): return text[len(prefix) :] return text # or whatever def sanitize(sd): return {remove_prefix(k, "model."): v for k, v in sd.items()} def average_state_dicts(state_dicts: List[Dict[str, torch.Tensor]]): new_sd = {} for k in state_dicts[0].keys(): tensors = [sd[k] for sd in state_dicts] new_t = sum(tensors) / len(tensors) assert isinstance(new_t, torch.Tensor) new_sd[k] = new_t return new_sd def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None: """Cleanup a pytorch-lightning .ckpt file or experiment dir and save a huggingface model with that state dict. Silently allows extra pl keys (like teacher.) Puts all ckpt models into CPU RAM at once! Args: pl_ckpt_path (:obj:`str`): Path to a .ckpt file saved by pytorch_lightning or dir containing ckpt files. If a directory is passed, all .ckpt files inside it will be averaged! hf_src_model_dir (:obj:`str`): Path to a directory containing a correctly shaped checkpoint save_path (:obj:`str`): Directory to save the new model """ hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir) if os.path.isfile(pl_ckpt_path): ckpt_files = [pl_ckpt_path] else: assert os.path.isdir(pl_ckpt_path) ckpt_files = list(Path(pl_ckpt_path).glob("*.ckpt")) assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory" if len(ckpt_files) > 1: logger.info(f"averaging the weights of {ckpt_files}") state_dicts = [sanitize(torch.load(x, map_location="cpu")["state_dict"]) for x in ckpt_files] state_dict = average_state_dicts(state_dicts) missing, unexpected = hf_model.load_state_dict(state_dict, strict=False) assert not missing, f"missing keys: {missing}" hf_model.save_pretrained(save_path) try: tok = AutoTokenizer.from_pretrained(hf_src_model_dir) tok.save_pretrained(save_path) except Exception: pass # dont copy tokenizer if cant if __name__ == "__main__": fire.Fire(convert_pl_to_hf)

transformers/examples/research_projects/seq2seq-distillation/convert_pl_checkpoint_to_hf.py/0

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#!/usr/bin/env bash export PYTHONPATH="../":"${PYTHONPATH}" export BS=32 export GAS=1 python finetune.py \ --learning_rate=3e-5 \ --fp16 \ --gpus 1 \ --do_train \ --do_predict \ --val_check_interval 0.25 \ --n_val 500 \ --num_train_epochs 2 \ --freeze_encoder --freeze_embeds --data_dir cnn_dm \ --max_target_length 142 --val_max_target_length=142 \ --train_batch_size=$BS --eval_batch_size=$BS --gradient_accumulation_steps=$GAS \ --model_name_or_path sshleifer/student_cnn_12_6 \ --tokenizer_name facebook/bart-large \ --warmup_steps 500 \ --output_dir distilbart-cnn-12-6 \ "$@"

transformers/examples/research_projects/seq2seq-distillation/train_distilbart_cnn.sh/0

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#!/usr/bin/env bash python run_asr.py \ --output_dir="./wav2vec2-large-lv60-timit-asr" \ --num_train_epochs="30" \ --per_device_train_batch_size="2" \ --per_device_eval_batch_size="2" \ --gradient_accumulation_steps="4" \ --evaluation_strategy="steps" \ --save_steps="500" \ --eval_steps="100" \ --logging_steps="50" \ --learning_rate="5e-4" \ --warmup_steps="3000" \ --model_name_or_path="facebook/wav2vec2-large-lv60" \ --fp16 \ --dataset_name="timit_asr" \ --train_split_name="train" \ --validation_split_name="test" \ --orthography="timit" \ --preprocessing_num_workers="$(nproc)" \ --group_by_length \ --freeze_feature_extractor \ --verbose_logging \

transformers/examples/research_projects/wav2vec2/finetune_large_lv60_timit_asr.sh/0

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# Examples This folder contains actively maintained examples of the use of 🤗 Transformers organized into different ML tasks. All examples in this folder are **TensorFlow** examples and are written using native Keras. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement! In addition, all scripts here now support the [🤗 Datasets](https://github.com/huggingface/datasets) library - you can grab entire datasets just by changing one command-line argument! ## A note on code folding Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult to follow, we highly recommend beginning with all regions folded and then examining regions one at a time! ## The Big Table of Tasks Here is the list of all our examples: | Task | Example datasets | |---|---| | [**`language-modeling`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling) | WikiText-2 | [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice) | SWAG | [**`question-answering`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering) | SQuAD | [**`summarization`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization) | XSum | [**`text-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification) | GLUE | [**`token-classification`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification) | CoNLL NER | [**`translation`**](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation) | WMT ## Coming soon - **Colab notebooks** to easily run through these scripts!

transformers/examples/tensorflow/README.md/0

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#!/usr/bin/env python # coding=utf-8 # Copyright 2023 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. """Script for training a Unigram tokenizer.""" import argparse import logging import datasets from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors from tokenizers.models import Unigram from tokenizers.trainers import UnigramTrainer from transformers import AlbertTokenizerFast logger = logging.getLogger(__name__) def parse_args(): parser = argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset.") parser.add_argument( "--dataset_name", type=str, default="wikitext", help="Name of the training. Explore datasets at: hf.co/datasets.", ) parser.add_argument( "--dataset_config", type=str, default="wikitext-103-raw-v1", help="Configuration name of the dataset." ) parser.add_argument( "--batch_size", type=int, default=1000, help="Batch size during training.", ) parser.add_argument( "--vocab_size", type=int, default=10048, help="Size of the desired vocabulary.", ) parser.add_argument( "--limit", default=None, type=int, help="Limit the number of shards (used for debugging).", ) parser.add_argument( "--export_to_hub", action="store_true", ) args = parser.parse_args() return args def main(args): dataset = datasets.load_dataset(args.dataset_name, args.dataset_config, split="train") if args.limit is not None: max_train_samples = min(len(dataset), args.limit) dataset = dataset.select(range(max_train_samples)) logger.info(f"Limiting the dataset to {args.limit} entries.") def batch_iterator(): for i in range(0, len(dataset), args.batch_size): yield dataset[i : i + args.batch_size]["text"] # Prepare the tokenizer. tokenizer = Tokenizer(Unigram()) tokenizer.normalizer = normalizers.Sequence([normalizers.Replace("``", '"'), normalizers.Replace("''", '"')]) tokenizer.pre_tokenizer = pre_tokenizers.Metaspace() # Prepare the trainer. trainer = UnigramTrainer( unk_token="<unk>", special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"], vocab_size=args.vocab_size, ) logger.info("Training the tokenizer.") tokenizer.train_from_iterator(batch_iterator(), trainer=trainer) logger.info("Tokenizer training complete!") cls_token_id = tokenizer.token_to_id("[CLS]") sep_token_id = tokenizer.token_to_id("[SEP]") tokenizer.post_processor = processors.TemplateProcessing( single="[CLS]:0 $A:0 [SEP]:0", pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1", special_tokens=[ ("[CLS]", cls_token_id), ("[SEP]", sep_token_id), ], ) tokenizer.decoder = decoders.Metaspace() if args.export_to_hub: logger.info("Exporting the trained tokenizer to Hub.") new_tokenizer = AlbertTokenizerFast(tokenizer_object=tokenizer) new_tokenizer.push_to_hub("unigram-tokenizer-dataset") if __name__ == "__main__": args = parse_args() main(args)

transformers/examples/tensorflow/language-modeling-tpu/train_unigram.py/0

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# Text classification examples This folder contains some scripts showing examples of *text classification* with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. ## run_text_classification.py This script handles perhaps the single most common use-case for this entire library: Training an NLP classifier on your own training data. This can be whatever you want - you could classify text as abusive/hateful or allowable, or forum posts as spam or not-spam, or classify the genre of a headline as politics, sports or any number of other categories. Any task that involves classifying natural language into two or more different categories can work with this! You can even do regression, such as predicting the score on a 1-10 scale that a user gave, given the text of their review. The preferred input format is either a CSV or newline-delimited JSON file that contains a `sentence1` and `label` field. If your task involves comparing two texts (for example, if your classifier is deciding whether two sentences are paraphrases of each other, or were written by the same author) then you should also include a `sentence2` field in each example. If you do not have a `sentence1` field then the script will assume the non-label fields are the input text, which may not always be what you want, especially if you have more than two fields! Here is a snippet of a valid input JSON file, though note that your texts can be much longer than these, and are not constrained (despite the field name) to being single grammatical sentences: ```json {"sentence1": "COVID-19 vaccine updates: How is the rollout proceeding?", "label": "news"} {"sentence1": "Manchester United celebrates Europa League success", "label": "sports"} ``` ### Usage notes If your inputs are long (more than ~60-70 words), you may wish to increase the `--max_seq_length` argument beyond the default value of 128. The maximum supported value for most models is 512 (about 200-300 words), and some can handle even longer. This will come at a cost in runtime and memory use, however. We assume that your labels represent *categories*, even if they are integers, since text classification is a much more common task than text regression. If your labels are floats, however, the script will assume you want to do regression. This is something you can edit yourself if your use-case requires it! After training, the model will be saved to `--output_dir`. Once your model is trained, you can get predictions by calling the script without a `--train_file` or `--validation_file`; simply pass it the output_dir containing the trained model and a `--test_file` and it will write its predictions to a text file for you. ### Multi-GPU and TPU usage By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. ### Memory usage and data loading One thing to note is that all data is loaded into memory in this script. Most text classification datasets are small enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command ```bash python run_text_classification.py \ --model_name_or_path distilbert/distilbert-base-cased \ --train_file training_data.json \ --validation_file validation_data.json \ --output_dir output/ \ --test_file data_to_predict.json \ --do_train \ --do_eval \ --do_predict ``` ## run_glue.py This script handles training on the GLUE dataset for various text classification and regression tasks. The GLUE datasets will be loaded automatically, so you only need to specify the task you want (with the `--task_name` argument). You can also supply your own files for prediction with the `--predict_file` argument, for example if you want to train a model on GLUE for e.g. paraphrase detection and then predict whether your own data contains paraphrases or not. Please ensure the names of your input fields match the names of the features in the relevant GLUE dataset - you can see a list of the column names in the `task_to_keys` dict in the `run_glue.py` file. ### Usage notes The `--do_train`, `--do_eval` and `--do_predict` arguments control whether training, evaluations or predictions are performed. After training, the model will be saved to `--output_dir`. Once your model is trained, you can call the script without the `--do_train` or `--do_eval` arguments to quickly get predictions from your saved model. ### Multi-GPU and TPU usage By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. ### Memory usage and data loading One thing to note is that all data is loaded into memory in this script. Most text classification datasets are small enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and README, but for more information you can see the 'Input Datasets' section of [this document](https://www.tensorflow.org/guide/tpu). ### Example command ```bash python run_glue.py \ --model_name_or_path distilbert/distilbert-base-cased \ --task_name mnli \ --do_train \ --do_eval \ --do_predict \ --predict_file data_to_predict.json ```

transformers/examples/tensorflow/text-classification/README.md/0

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#!/usr/bin/env python # # This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or # many nodes) can talk to each other via nccl and allocate gpu memory. # # To run first adjust the number of processes and nodes: # # python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py # # You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port # # You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d # # use torch.distributed.launch instead of torch.distributed.run for torch < 1.9 # # If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with: # # NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py # # which should tell you what's going on behind the scenes. # # # This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that # runs on 2 nodes of 4 gpus per node: # # #SBATCH --job-name=test-nodes # name # #SBATCH --nodes=2 # nodes # #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! # #SBATCH --cpus-per-task=10 # number of cores per tasks # #SBATCH --gres=gpu:4 # number of gpus # #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS) # #SBATCH --output=%x-%j.out # output file name # # GPUS_PER_NODE=4 # MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1) # MASTER_PORT=6000 # # srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \ # --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \ # --master_addr $MASTER_ADDR --master_port $MASTER_PORT \ # torch-distributed-gpu-test.py' # import fcntl import os import socket import torch import torch.distributed as dist def printflock(*msgs): """solves multi-process interleaved print problem""" with open(__file__, "r") as fh: fcntl.flock(fh, fcntl.LOCK_EX) try: print(*msgs) finally: fcntl.flock(fh, fcntl.LOCK_UN) local_rank = int(os.environ["LOCAL_RANK"]) torch.cuda.set_device(local_rank) device = torch.device("cuda", local_rank) hostname = socket.gethostname() gpu = f"[{hostname}-{local_rank}]" try: # test distributed dist.init_process_group("nccl") dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM) dist.barrier() # test cuda is available and can allocate memory torch.cuda.is_available() torch.ones(1).cuda(local_rank) # global rank rank = dist.get_rank() world_size = dist.get_world_size() printflock(f"{gpu} is OK (global rank: {rank}/{world_size})") dist.barrier() if rank == 0: printflock(f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}") except Exception: printflock(f"{gpu} is broken") raise

transformers/scripts/distributed/torch-distributed-gpu-test.py/0

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Setup transformers following instructions in README.md, (I would fork first). ```bash git clone [email protected]:huggingface/transformers.git cd transformers pip install -e . pip install pandas GitPython wget ``` Get required metadata ```bash curl https://cdn-datasets.huggingface.co/language_codes/language-codes-3b2.csv > language-codes-3b2.csv curl https://cdn-datasets.huggingface.co/language_codes/iso-639-3.csv > iso-639-3.csv ``` Install Tatoeba-Challenge repo inside transformers ```bash git clone [email protected]:Helsinki-NLP/Tatoeba-Challenge.git ``` To convert a few models, call the conversion script from command line: ```bash python src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py --models heb-eng eng-heb --save_dir converted ``` To convert lots of models you can pass your list of Tatoeba model names to `resolver.convert_models` in a python client or script. ```python from transformers.convert_marian_tatoeba_to_pytorch import TatoebaConverter resolver = TatoebaConverter(save_dir='converted') resolver.convert_models(['heb-eng', 'eng-heb']) ``` ### Upload converted models Since version v3.5.0, the model sharing workflow is switched to git-based system . Refer to [model sharing doc](https://huggingface.co/transformers/main/model_sharing.html#model-sharing-and-uploading) for more details. To upload all converted models, 1. Install [git-lfs](https://git-lfs.github.com/). 2. Login to `huggingface-cli` ```bash huggingface-cli login ``` 3. Run the `upload_models` script ```bash ./scripts/tatoeba/upload_models.sh ``` ### Modifications - To change naming logic, change the code near `os.rename`. The model card creation code may also need to change. - To change model card content, you must modify `TatoebaCodeResolver.write_model_card`

transformers/scripts/tatoeba/README.md/0

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# Copyright 2020 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. import json import os import shutil import warnings from argparse import ArgumentParser, Namespace from pathlib import Path from typing import List from ..utils import logging from . import BaseTransformersCLICommand try: from cookiecutter.main import cookiecutter _has_cookiecutter = True except ImportError: _has_cookiecutter = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name def add_new_model_command_factory(args: Namespace): return AddNewModelCommand(args.testing, args.testing_file, path=args.path) class AddNewModelCommand(BaseTransformersCLICommand): @staticmethod def register_subcommand(parser: ArgumentParser): add_new_model_parser = parser.add_parser("add-new-model") add_new_model_parser.add_argument("--testing", action="store_true", help="If in testing mode.") add_new_model_parser.add_argument("--testing_file", type=str, help="Configuration file on which to run.") add_new_model_parser.add_argument( "--path", type=str, help="Path to cookiecutter. Should only be used for testing purposes." ) add_new_model_parser.set_defaults(func=add_new_model_command_factory) def __init__(self, testing: bool, testing_file: str, path=None, *args): self._testing = testing self._testing_file = testing_file self._path = path def run(self): warnings.warn( "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. " "It is not actively maintained anymore, so might give a result that won't pass all tests and quality " "checks, you should use `transformers-cli add-new-model-like` instead." ) if not _has_cookiecutter: raise ImportError( "Model creation dependencies are required to use the `add_new_model` command. Install them by running " "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" ) # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory directories = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]] if len(directories) > 0: raise ValueError( "Several directories starting with `cookiecutter-template-` in current working directory. " "Please clean your directory by removing all folders starting with `cookiecutter-template-` or " "change your working directory." ) path_to_transformer_root = ( Path(__file__).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent ) path_to_cookiecutter = path_to_transformer_root / "templates" / "adding_a_new_model" # Execute cookiecutter if not self._testing: cookiecutter(str(path_to_cookiecutter)) else: with open(self._testing_file, "r") as configuration_file: testing_configuration = json.load(configuration_file) cookiecutter( str(path_to_cookiecutter if self._path is None else self._path), no_input=True, extra_context=testing_configuration, ) directory = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0] # Retrieve configuration with open(directory + "/configuration.json", "r") as configuration_file: configuration = json.load(configuration_file) lowercase_model_name = configuration["lowercase_modelname"] generate_tensorflow_pytorch_and_flax = configuration["generate_tensorflow_pytorch_and_flax"] os.remove(f"{directory}/configuration.json") output_pytorch = "PyTorch" in generate_tensorflow_pytorch_and_flax output_tensorflow = "TensorFlow" in generate_tensorflow_pytorch_and_flax output_flax = "Flax" in generate_tensorflow_pytorch_and_flax model_dir = f"{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}" os.makedirs(model_dir, exist_ok=True) os.makedirs(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}", exist_ok=True) # Tests require submodules as they have parent imports with open(f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py", "w"): pass shutil.move( f"{directory}/__init__.py", f"{model_dir}/__init__.py", ) shutil.move( f"{directory}/configuration_{lowercase_model_name}.py", f"{model_dir}/configuration_{lowercase_model_name}.py", ) def remove_copy_lines(path): with open(path, "r") as f: lines = f.readlines() with open(path, "w") as f: for line in lines: if "# Copied from transformers." not in line: f.write(line) if output_pytorch: if not self._testing: remove_copy_lines(f"{directory}/modeling_{lowercase_model_name}.py") shutil.move( f"{directory}/modeling_{lowercase_model_name}.py", f"{model_dir}/modeling_{lowercase_model_name}.py", ) shutil.move( f"{directory}/test_modeling_{lowercase_model_name}.py", f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py", ) else: os.remove(f"{directory}/modeling_{lowercase_model_name}.py") os.remove(f"{directory}/test_modeling_{lowercase_model_name}.py") if output_tensorflow: if not self._testing: remove_copy_lines(f"{directory}/modeling_tf_{lowercase_model_name}.py") shutil.move( f"{directory}/modeling_tf_{lowercase_model_name}.py", f"{model_dir}/modeling_tf_{lowercase_model_name}.py", ) shutil.move( f"{directory}/test_modeling_tf_{lowercase_model_name}.py", f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py", ) else: os.remove(f"{directory}/modeling_tf_{lowercase_model_name}.py") os.remove(f"{directory}/test_modeling_tf_{lowercase_model_name}.py") if output_flax: if not self._testing: remove_copy_lines(f"{directory}/modeling_flax_{lowercase_model_name}.py") shutil.move( f"{directory}/modeling_flax_{lowercase_model_name}.py", f"{model_dir}/modeling_flax_{lowercase_model_name}.py", ) shutil.move( f"{directory}/test_modeling_flax_{lowercase_model_name}.py", f"{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py", ) else: os.remove(f"{directory}/modeling_flax_{lowercase_model_name}.py") os.remove(f"{directory}/test_modeling_flax_{lowercase_model_name}.py") shutil.move( f"{directory}/{lowercase_model_name}.md", f"{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md", ) shutil.move( f"{directory}/tokenization_{lowercase_model_name}.py", f"{model_dir}/tokenization_{lowercase_model_name}.py", ) shutil.move( f"{directory}/tokenization_fast_{lowercase_model_name}.py", f"{model_dir}/tokenization_{lowercase_model_name}_fast.py", ) from os import fdopen, remove from shutil import copymode, move from tempfile import mkstemp def replace(original_file: str, line_to_copy_below: str, lines_to_copy: List[str]): # Create temp file fh, abs_path = mkstemp() line_found = False with fdopen(fh, "w") as new_file: with open(original_file) as old_file: for line in old_file: new_file.write(line) if line_to_copy_below in line: line_found = True for line_to_copy in lines_to_copy: new_file.write(line_to_copy) if not line_found: raise ValueError(f"Line {line_to_copy_below} was not found in file.") # Copy the file permissions from the old file to the new file copymode(original_file, abs_path) # Remove original file remove(original_file) # Move new file move(abs_path, original_file) def skip_units(line): return ( ("generating PyTorch" in line and not output_pytorch) or ("generating TensorFlow" in line and not output_tensorflow) or ("generating Flax" in line and not output_flax) ) def replace_in_files(path_to_datafile): with open(path_to_datafile) as datafile: lines_to_copy = [] skip_file = False skip_snippet = False for line in datafile: if "# To replace in: " in line and "##" not in line: file_to_replace_in = line.split('"')[1] skip_file = skip_units(line) elif "# Below: " in line and "##" not in line: line_to_copy_below = line.split('"')[1] skip_snippet = skip_units(line) elif "# End." in line and "##" not in line: if not skip_file and not skip_snippet: replace(file_to_replace_in, line_to_copy_below, lines_to_copy) lines_to_copy = [] elif "# Replace with" in line and "##" not in line: lines_to_copy = [] elif "##" not in line: lines_to_copy.append(line) remove(path_to_datafile) replace_in_files(f"{directory}/to_replace_{lowercase_model_name}.py") os.rmdir(directory)

transformers/src/transformers/commands/add_new_model.py/0

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# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)""" import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS} def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download): if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES: raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.") if tokenizer_name is None: tokenizer_names = TOKENIZER_CLASSES else: tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")} logger.info(f"Loading tokenizer classes: {tokenizer_names}") for tokenizer_name in tokenizer_names: tokenizer_class = TOKENIZER_CLASSES[tokenizer_name] add_prefix = True if checkpoint_name is None: checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys()) else: checkpoint_names = [checkpoint_name] logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}") for checkpoint in checkpoint_names: logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}") # Load tokenizer tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download) # Save fast tokenizer logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}") # For organization names we create sub-directories if "/" in checkpoint: checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/") dump_path_full = os.path.join(dump_path, checkpoint_directory) elif add_prefix: checkpoint_prefix_name = checkpoint dump_path_full = dump_path else: checkpoint_prefix_name = None dump_path_full = dump_path logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]: file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint] next_char = file_path.split(checkpoint)[-1][0] if next_char == "/": dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name) checkpoint_prefix_name = None logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") file_names = tokenizer.save_pretrained( dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name ) logger.info(f"=> File names {file_names}") for file_name in file_names: if not file_name.endswith("tokenizer.json"): os.remove(file_name) logger.info(f"=> removing {file_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files." ) parser.add_argument( "--tokenizer_name", default=None, type=str, help=( f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will " "download and convert all the checkpoints from AWS." ), ) parser.add_argument( "--checkpoint_name", default=None, type=str, help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.", ) parser.add_argument( "--force_download", action="store_true", help="Re-download checkpoints.", ) args = parser.parse_args() convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

transformers/src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py/0

{ "file_path": "transformers/src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py", "repo_id": "transformers", "token_count": 1993 }

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# Copyright 2020 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. """ Integration with Deepspeed - kept for backward compatiblity, if you plan to make any edit, make sure to modify the file in `integrations/deepspeed` instead. Check: https://github.com/huggingface/transformers/pull/25599 """ import warnings warnings.warn( "transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations", FutureWarning, ) # Backward compatibility imports, to make sure all those objects can be found in integrations/deepspeed from .integrations.deepspeed import ( # noqa HfDeepSpeedConfig, HfTrainerDeepSpeedConfig, deepspeed_config, deepspeed_init, deepspeed_load_checkpoint, deepspeed_optim_sched, is_deepspeed_available, is_deepspeed_zero3_enabled, set_hf_deepspeed_config, unset_hf_deepspeed_config, )

transformers/src/transformers/deepspeed.py/0

{ "file_path": "transformers/src/transformers/deepspeed.py", "repo_id": "transformers", "token_count": 432 }

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# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from queue import Queue from typing import TYPE_CHECKING, Optional if TYPE_CHECKING: from ..models.auto import AutoTokenizer class BaseStreamer: """ Base class from which `.generate()` streamers should inherit. """ def put(self, value): """Function that is called by `.generate()` to push new tokens""" raise NotImplementedError() def end(self): """Function that is called by `.generate()` to signal the end of generation""" raise NotImplementedError() class TextStreamer(BaseStreamer): """ Simple text streamer that prints the token(s) to stdout as soon as entire words are formed. <Tip warning={true}> The API for the streamer classes is still under development and may change in the future. </Tip> Parameters: tokenizer (`AutoTokenizer`): The tokenized used to decode the tokens. skip_prompt (`bool`, *optional*, defaults to `False`): Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots. decode_kwargs (`dict`, *optional*): Additional keyword arguments to pass to the tokenizer's `decode` method. Examples: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer >>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") >>> streamer = TextStreamer(tok) >>> # Despite returning the usual output, the streamer will also print the generated text to stdout. >>> _ = model.generate(**inputs, streamer=streamer, max_new_tokens=20) An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven, ``` """ def __init__(self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, **decode_kwargs): self.tokenizer = tokenizer self.skip_prompt = skip_prompt self.decode_kwargs = decode_kwargs # variables used in the streaming process self.token_cache = [] self.print_len = 0 self.next_tokens_are_prompt = True def put(self, value): """ Receives tokens, decodes them, and prints them to stdout as soon as they form entire words. """ if len(value.shape) > 1 and value.shape[0] > 1: raise ValueError("TextStreamer only supports batch size 1") elif len(value.shape) > 1: value = value[0] if self.skip_prompt and self.next_tokens_are_prompt: self.next_tokens_are_prompt = False return # Add the new token to the cache and decodes the entire thing. self.token_cache.extend(value.tolist()) text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs) # After the symbol for a new line, we flush the cache. if text.endswith("\n"): printable_text = text[self.print_len :] self.token_cache = [] self.print_len = 0 # If the last token is a CJK character, we print the characters. elif len(text) > 0 and self._is_chinese_char(ord(text[-1])): printable_text = text[self.print_len :] self.print_len += len(printable_text) # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words, # which may change with the subsequent token -- there are probably smarter ways to do this!) else: printable_text = text[self.print_len : text.rfind(" ") + 1] self.print_len += len(printable_text) self.on_finalized_text(printable_text) def end(self): """Flushes any remaining cache and prints a newline to stdout.""" # Flush the cache, if it exists if len(self.token_cache) > 0: text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs) printable_text = text[self.print_len :] self.token_cache = [] self.print_len = 0 else: printable_text = "" self.next_tokens_are_prompt = True self.on_finalized_text(printable_text, stream_end=True) def on_finalized_text(self, text: str, stream_end: bool = False): """Prints the new text to stdout. If the stream is ending, also prints a newline.""" print(text, flush=True, end="" if not stream_end else None) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False class TextIteratorStreamer(TextStreamer): """ Streamer that stores print-ready text in a queue, to be used by a downstream application as an iterator. This is useful for applications that benefit from acessing the generated text in a non-blocking way (e.g. in an interactive Gradio demo). <Tip warning={true}> The API for the streamer classes is still under development and may change in the future. </Tip> Parameters: tokenizer (`AutoTokenizer`): The tokenized used to decode the tokens. skip_prompt (`bool`, *optional*, defaults to `False`): Whether to skip the prompt to `.generate()` or not. Useful e.g. for chatbots. timeout (`float`, *optional*): The timeout for the text queue. If `None`, the queue will block indefinitely. Useful to handle exceptions in `.generate()`, when it is called in a separate thread. decode_kwargs (`dict`, *optional*): Additional keyword arguments to pass to the tokenizer's `decode` method. Examples: ```python >>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer >>> from threading import Thread >>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2") >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") >>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt") >>> streamer = TextIteratorStreamer(tok) >>> # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way. >>> generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20) >>> thread = Thread(target=model.generate, kwargs=generation_kwargs) >>> thread.start() >>> generated_text = "" >>> for new_text in streamer: ... generated_text += new_text >>> generated_text 'An increasing sequence: one, two, three, four, five, six, seven, eight, nine, ten, eleven,' ``` """ def __init__( self, tokenizer: "AutoTokenizer", skip_prompt: bool = False, timeout: Optional[float] = None, **decode_kwargs ): super().__init__(tokenizer, skip_prompt, **decode_kwargs) self.text_queue = Queue() self.stop_signal = None self.timeout = timeout def on_finalized_text(self, text: str, stream_end: bool = False): """Put the new text in the queue. If the stream is ending, also put a stop signal in the queue.""" self.text_queue.put(text, timeout=self.timeout) if stream_end: self.text_queue.put(self.stop_signal, timeout=self.timeout) def __iter__(self): return self def __next__(self): value = self.text_queue.get(timeout=self.timeout) if value == self.stop_signal: raise StopIteration() else: return value

transformers/src/transformers/generation/streamers.py/0

{ "file_path": "transformers/src/transformers/generation/streamers.py", "repo_id": "transformers", "token_count": 3625 }

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# Copyright 2020 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. """ Integration with Deepspeed """ import copy import importlib.metadata as importlib_metadata import importlib.util import weakref from functools import partialmethod from ..dependency_versions_check import dep_version_check from ..utils import is_accelerate_available, is_torch_available, logging if is_torch_available(): import torch logger = logging.get_logger(__name__) def is_deepspeed_available(): package_exists = importlib.util.find_spec("deepspeed") is not None # Check we're not importing a "deepspeed" directory somewhere but the actual library by trying to grab the version # AND checking it has an author field in the metadata that is HuggingFace. if package_exists: try: _ = importlib_metadata.metadata("deepspeed") return True except importlib_metadata.PackageNotFoundError: return False if is_accelerate_available() and is_deepspeed_available(): from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig else: # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file. # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available. from builtins import object as DeepSpeedConfig class HfDeepSpeedConfig(DeepSpeedConfig): """ This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage. A `weakref` of this object is stored in the module's globals to be able to access the config from areas where things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore it's important that this object remains alive while the program is still running. [`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic the DeepSpeed configuration is not modified in any way. Args: config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict. """ def __init__(self, config_file_or_dict): # set global weakref object set_hf_deepspeed_config(self) dep_version_check("accelerate") dep_version_check("deepspeed") super().__init__(config_file_or_dict) class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig): """ The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the same lifespan as the latter. """ def __init__(self, config_file_or_dict): super().__init__(config_file_or_dict) self._dtype = None self.mismatches = [] def dtype(self): if self._dtype is None: raise ValueError("trainer_config_process() wasn't called yet to tell dtype") return self._dtype def is_auto(self, ds_key_long): val = self.get_value(ds_key_long) if val is None: return False else: return val == "auto" def fill_match(self, ds_key_long, hf_val, hf_key=None, must_match=True): """ A utility method that massages the config file and can optionally verify that the values match. 1. Replace "auto" values with `TrainingArguments` value. 2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer config values and if mismatched add the entry to `self.mismatched` - will assert during `trainer_config_finalize` for one or more mismatches. """ config, ds_key = self.find_config_node(ds_key_long) if config is None: return if config.get(ds_key) == "auto": config[ds_key] = hf_val return if not must_match: return ds_val = config.get(ds_key) if ds_val is not None and ds_val != hf_val: self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}") fill_only = partialmethod(fill_match, must_match=False) def trainer_config_process(self, args, auto_find_batch_size=False): """ Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object creation. """ # DeepSpeed does: # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps self.fill_match( "train_micro_batch_size_per_gpu", args.per_device_train_batch_size, "per_device_train_batch_size", not auto_find_batch_size, ) self.fill_match( "gradient_accumulation_steps", args.gradient_accumulation_steps, "gradient_accumulation_steps", ) self.fill_match( "train_batch_size", train_batch_size, "train_batch_size (calculated)", not auto_find_batch_size, ) self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm") self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate") self.fill_match( "optimizer.params.betas", [args.adam_beta1, args.adam_beta2], "adam_beta1+adam_beta2", ) self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon") self.fill_match("optimizer.params.weight_decay", args.weight_decay, "weight_decay") self.fill_only("scheduler.params.warmup_min_lr", 0) # not a trainer arg self.fill_match("scheduler.params.warmup_max_lr", args.learning_rate, "learning_rate") # total_num_steps - will get set in trainer_config_finalize # fp16 if args.fp16 or args.fp16_full_eval: fp16_backend = "apex" if args.fp16_backend == "apex" else "amp" else: fp16_backend = None if args.save_on_each_node: # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True self.config["checkpoint"] = self.config.get("checkpoint", {}) self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set # any here unless the user did the work self.fill_match( "fp16.enabled", ((args.fp16 or args.fp16_full_eval) and fp16_backend == "amp"), "fp16|fp16_full_eval+fp16_backend(amp)", ) # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any # ZeRO features self.fill_match("amp.enabled", fp16_backend == "apex", "fp16+fp16_backend(apex)") self.fill_match("amp.opt_level", args.fp16_opt_level, "fp16_opt_level") self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval") # deepspeed's default mode is fp16 unless there is a config that says differently if self.is_true("bf16.enabled"): self._dtype = torch.bfloat16 elif self.is_false("fp16.enabled"): self._dtype = torch.float32 else: self._dtype = torch.float16 def trainer_config_finalize(self, args, model, num_training_steps): """ This stage is run after we have the model and know num_training_steps. Now we can complete the configuration process. """ # zero # deal with config keys that use `auto` value and rely on model's hidden_size hidden_size_based_keys = [ "zero_optimization.reduce_bucket_size", "zero_optimization.stage3_prefetch_bucket_size", "zero_optimization.stage3_param_persistence_threshold", ] hidden_size_auto_keys = [x for x in hidden_size_based_keys if self.is_auto(x)] if len(hidden_size_auto_keys) > 0: if hasattr(model.config, "hidden_size"): hidden_size = model.config.hidden_size elif hasattr(model.config, "hidden_sizes"): # if there are many hidden sizes pick the largest one hidden_size = max(model.config.hidden_sizes) else: raise ValueError( "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, " "therefore it's not possible to automatically fill out the following `auto` entries " f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing " "`auto` values for these keys with an integer value of your choice." ) self.fill_only("zero_optimization.reduce_bucket_size", hidden_size * hidden_size) if self.is_zero3(): # automatically assign the optimal config values based on model config self.fill_only( "zero_optimization.stage3_prefetch_bucket_size", 0.9 * hidden_size * hidden_size, ) self.fill_only( "zero_optimization.stage3_param_persistence_threshold", 10 * hidden_size, ) # scheduler self.fill_match( "scheduler.params.total_num_steps", num_training_steps, "num_training_steps (calculated)", ) self.fill_match( "scheduler.params.warmup_num_steps", args.get_warmup_steps(num_training_steps), "warmup_steps", ) if len(self.mismatches) > 0: mismatches = "\n".join(self.mismatches) raise ValueError( "Please correct the following DeepSpeed config values that mismatch TrainingArguments" f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'." ) # keep the config object global to be able to access it anywhere during TrainingArguments life-cycle _hf_deepspeed_config_weak_ref = None def set_hf_deepspeed_config(hf_deepspeed_config_obj): # this is a special weakref global object to allow us to get to Deepspeed config from APIs # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain. global _hf_deepspeed_config_weak_ref # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed) _hf_deepspeed_config_weak_ref = weakref.ref(hf_deepspeed_config_obj) def unset_hf_deepspeed_config(): # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method global _hf_deepspeed_config_weak_ref _hf_deepspeed_config_weak_ref = None def is_deepspeed_zero3_enabled(): if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None: return _hf_deepspeed_config_weak_ref().is_zero3() else: return False def deepspeed_config(): if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None: return _hf_deepspeed_config_weak_ref().config else: return None def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps, model_parameters): """ A convenience wrapper that deals with optimizer and lr scheduler configuration. """ from accelerate.utils import DummyOptim, DummyScheduler config = hf_deepspeed_config.config # Mixing and matching DS schedulers and optimizers is supported unless Offload is enabled in which case it's: # 1. DS scheduler + DS optimizer: Yes # 2. HF scheduler + HF optimizer: Mostly* # 3. DS scheduler + HF optimizer: Mostly* # 4. HF scheduler + DS optimizer: Yes # # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB) optimizer = None if "optimizer" in config: if args.adafactor: raise ValueError( "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. " "Only one optimizer can be configured." ) optimizer = DummyOptim(params=model_parameters) else: if hf_deepspeed_config.is_offload(): logger.info( "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the" " custom optimizer has both CPU and GPU implementation (except LAMB)" ) # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch. # But trainer uses AdamW by default. optimizer = trainer.create_optimizer() # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer` config["zero_allow_untested_optimizer"] = True lr_scheduler = None if "scheduler" in config: lr_scheduler = DummyScheduler(optimizer) else: if isinstance(optimizer, DummyOptim): def _lr_scheduler_callable(optimizer): # create a shallow copy first, so later modifications do not affect original trainer trainer_copy = copy.copy(trainer) # at the time _lr_scheduler_callable is called, trainer.lr_scheduler has been set # update it to None so that we can re-create a new scheduler trainer_copy.lr_scheduler = None lr_scheduler = trainer_copy.create_scheduler( num_training_steps=num_training_steps, optimizer=optimizer ) return lr_scheduler lr_scheduler = DummyScheduler(optimizer, lr_scheduler_callable=_lr_scheduler_callable) else: lr_scheduler = trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer) return optimizer, lr_scheduler def deepspeed_init(trainer, num_training_steps, inference=False): """ Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args. If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made. Args: trainer: Trainer object num_training_steps: per single gpu resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load inference: launch in inference mode (no optimizer and no lr scheduler) auto_find_batch_size: whether to ignore the `train_micro_batch_size_per_gpu` argument as it's being set automatically by the auto batch size finder Returns: optimizer, lr_scheduler We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on: https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612 """ from deepspeed.utils import logger as ds_logger model = trainer.model args = trainer.args hf_deepspeed_config = trainer.accelerator.state.deepspeed_plugin.hf_ds_config # resume config update - some bits like `model` and `num_training_steps` only become available during train hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps) # set the Deepspeed log level consistent with the Trainer ds_logger.setLevel(args.get_process_log_level()) if inference: # only Z3 makes sense for the inference if not hf_deepspeed_config.is_zero3(): raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config") # in case the training config is re-used for inference hf_deepspeed_config.del_config_sub_tree("optimizer") hf_deepspeed_config.del_config_sub_tree("lr_scheduler") optimizer, lr_scheduler = None, None model_parameters = None else: trainer.optimizer = None # important for when deepspeed_init is used as re-init model_parameters = list(filter(lambda p: p.requires_grad, model.parameters())) optimizer, lr_scheduler = deepspeed_optim_sched( trainer, hf_deepspeed_config, args, num_training_steps, model_parameters ) # keep for quick debug: # from pprint import pprint; pprint(config) return optimizer, lr_scheduler def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path, load_module_strict=True): # it's possible that the user is trying to resume from model_path, which doesn't necessarily # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's # a resume from a checkpoint and not just a local pretrained weight. So we check here if the # path contains what looks like a deepspeed checkpoint import glob deepspeed_checkpoint_dirs = sorted(glob.glob(f"{checkpoint_path}/global_step*")) if len(deepspeed_checkpoint_dirs) > 0: logger.info(f"Attempting to resume from {checkpoint_path}") # this magically updates self.optimizer and self.lr_scheduler load_path, _ = deepspeed_engine.load_checkpoint( checkpoint_path, load_module_strict=load_module_strict, load_optimizer_states=True, load_lr_scheduler_states=True, ) if load_path is None: raise ValueError(f"[deepspeed] failed to resume from checkpoint {checkpoint_path}") else: raise ValueError(f"Can't find a valid checkpoint at {checkpoint_path}")

transformers/src/transformers/integrations/deepspeed.py/0

{ "file_path": "transformers/src/transformers/integrations/deepspeed.py", "repo_id": "transformers", "token_count": 7316 }

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#include "common.h" template<typename T> __device__ int set_insert(T *set, int set_size, T value) { int slot = value % set_size; int start_slot = slot; while (true) { T prev = atomicCAS(&set[slot], EMPTY_VALUE, value); if (prev == EMPTY_VALUE || prev == value) { return slot; } slot = (slot + 1) % set_size; if (slot == start_slot) { return -1; } } return -1; } template<typename T> __device__ int set_lookup(T *set, int set_size, T value) { int slot = value % set_size; int start_slot = slot; while (true) { if (set[slot] == value) { return slot; } slot = (slot + 1) % set_size; if (slot == start_slot) { return -1; } } return -1; } template<typename T> __device__ void init_buffer(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { __syncthreads(); for (int i = 0; i < buffer_size; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < buffer_size) { buffer[offset_idx] = init_value; } } __syncthreads(); } template<typename T> __device__ void copy_data(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { __syncthreads(); for (int i = 0; i < data_length; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < data_length) { dist_pt[offset_idx] = src_pt[offset_idx]; } } __syncthreads(); } template<typename T> __device__ void init_buffer_nonblocking(T init_value, T *buffer, int buffer_size, int num_threads, int thread_id) { for (int i = 0; i < buffer_size; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < buffer_size) { buffer[offset_idx] = init_value; } } } template<typename T> __device__ void copy_data_nonblocking(T *src_pt, T *dist_pt, int data_length, int num_threads, int thread_id) { for (int i = 0; i < data_length; i = i + num_threads) { int offset_idx = i + thread_id; if (offset_idx < data_length) { dist_pt[offset_idx] = src_pt[offset_idx]; } } }

transformers/src/transformers/kernels/yoso/common_cuda_device.h/0

{ "file_path": "transformers/src/transformers/kernels/yoso/common_cuda_device.h", "repo_id": "transformers", "token_count": 892 }

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# Copyright 2020 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. from . import ( albert, align, altclip, audio_spectrogram_transformer, auto, autoformer, bark, bart, barthez, bartpho, beit, bert, bert_generation, bert_japanese, bertweet, big_bird, bigbird_pegasus, biogpt, bit, blenderbot, blenderbot_small, blip, blip_2, bloom, bridgetower, bros, byt5, camembert, canine, chinese_clip, clap, clip, clipseg, clvp, code_llama, codegen, cohere, conditional_detr, convbert, convnext, convnextv2, cpm, cpmant, ctrl, cvt, data2vec, deberta, deberta_v2, decision_transformer, deformable_detr, deit, deprecated, depth_anything, deta, detr, dialogpt, dinat, dinov2, distilbert, dit, donut, dpr, dpt, efficientformer, efficientnet, electra, encodec, encoder_decoder, ernie, ernie_m, esm, falcon, fastspeech2_conformer, flaubert, flava, fnet, focalnet, fsmt, funnel, fuyu, gemma, git, glpn, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_sw3, gptj, gptsan_japanese, graphormer, groupvit, herbert, hubert, ibert, idefics, imagegpt, informer, instructblip, jukebox, kosmos2, layoutlm, layoutlmv2, layoutlmv3, layoutxlm, led, levit, lilt, llama, llava, llava_next, longformer, longt5, luke, lxmert, m2m_100, mamba, marian, markuplm, mask2former, maskformer, mbart, mbart50, mega, megatron_bert, megatron_gpt2, mgp_str, mistral, mixtral, mluke, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nezha, nllb, nllb_moe, nougat, nystromformer, oneformer, openai, opt, owlv2, owlvit, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, persimmon, phi, phobert, pix2struct, plbart, poolformer, pop2piano, prophetnet, pvt, pvt_v2, qdqbert, qwen2, rag, realm, reformer, regnet, rembert, resnet, roberta, roberta_prelayernorm, roc_bert, roformer, rwkv, sam, seamless_m4t, seamless_m4t_v2, segformer, seggpt, sew, sew_d, siglip, speech_encoder_decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, table_transformer, tapas, time_series_transformer, timesformer, timm_backbone, trocr, tvlt, tvp, udop, umt5, unispeech, unispeech_sat, univnet, upernet, videomae, vilt, vipllava, vision_encoder_decoder, vision_text_dual_encoder, visual_bert, vit, vit_hybrid, vit_mae, vit_msn, vitdet, vitmatte, vits, vivit, wav2vec2, wav2vec2_bert, wav2vec2_conformer, wav2vec2_phoneme, wav2vec2_with_lm, wavlm, whisper, x_clip, xglm, xlm, xlm_prophetnet, xlm_roberta, xlm_roberta_xl, xlnet, xmod, yolos, yoso, )

transformers/src/transformers/models/__init__.py/0

{ "file_path": "transformers/src/transformers/models/__init__.py", "repo_id": "transformers", "token_count": 2211 }

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# coding=utf-8 # Copyright 2022 The BAAI Teams Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch AltCLIP model.""" import math from dataclasses import dataclass from typing import Any, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions, BaseModelOutputWithPoolingAndProjection, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ModelOutput, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_altclip import AltCLIPConfig, AltCLIPTextConfig, AltCLIPVisionConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "BAAI/AltCLIP" _CONFIG_FOR_DOC = "AltCLIPConfig" ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "BAAI/AltCLIP", # See all AltCLIP models at https://huggingface.co/models?filter=altclip ] ALTCLIP_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 ([`CLIPConfig`]): 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. """ ALTCLIP_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. """ ALTCLIP_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. """ ALTCLIP_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. """ # contrastive loss function, adapted from # https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) def clip_loss(similarity: torch.Tensor) -> torch.Tensor: caption_loss = contrastive_loss(similarity) image_loss = contrastive_loss(similarity.t()) return (caption_loss + image_loss) / 2.0 @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->AltCLIP class AltCLIPOutput(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 [`AltCLIPTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`AltCLIPVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`AltCLIPTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`AltCLIPVisionModel`]. """ 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() ) # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->AltRoberta class AltRobertaEmbeddings(nn.Module): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__ def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) # End copy self.padding_idx = config.pad_token_id self.position_embeddings = nn.Embedding( config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 ): if position_ids is None: if input_ids is not None: # Create the position ids from the input token ids. Any padded tokens remain padded. position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length) else: position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings def create_position_ids_from_inputs_embeds(self, inputs_embeds): """ We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor """ input_shape = inputs_embeds.size()[:-1] sequence_length = input_shape[1] position_ids = torch.arange( self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device ) return position_ids.unsqueeze(0).expand(input_shape) # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfAttention with Roberta->AltRoberta class AltRobertaSelfAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = position_embedding_type or getattr( config, "position_embedding_type", "absolute" ) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) self.is_decoder = config.is_decoder def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: mixed_query_layer = self.query(hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) use_cache = past_key_value is not None if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": query_length, key_length = query_layer.shape[2], key_layer.shape[2] if use_cache: position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view( -1, 1 ) else: position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in AltRobertaModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaSelfOutput class AltRobertaSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaAttention with Roberta->AltRoberta class AltRobertaAttention(nn.Module): def __init__(self, config, position_embedding_type=None): super().__init__() self.self = AltRobertaSelfAttention(config, position_embedding_type=position_embedding_type) self.output = AltRobertaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self( hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.roberta.modeling_roberta.RobertaIntermediate with Roberta->AltRoberta class AltRobertaIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaOutput class AltRobertaOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.roberta.modeling_roberta.RobertaLayer with Roberta->AltRoberta class AltRobertaLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = AltRobertaAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = AltRobertaAttention(config, position_embedding_type="absolute") self.intermediate = AltRobertaIntermediate(config) self.output = AltRobertaOutput(config) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output # Copied from transformers.models.roberta.modeling_roberta.RobertaEncoder with Roberta->AltRoberta class AltRobertaEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.layer = nn.ModuleList([AltRobertaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None 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 next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.roberta.modeling_roberta.RobertaPooler class AltRobertaPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output # Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->AltCLIP class AltCLIPAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" 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 _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, causal_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""" bsz, tgt_len, embed_dim = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scale key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.view(*proj_shape) value_states = value_states.view(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) # apply the causal_attention_mask first if causal_attention_mask is not None: if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is" f" {causal_attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if output_attentions: # this operation is a bit akward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, tgt_len, embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->AltCLIP class AltCLIPMLP(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->AltCLIP class AltCLIPEncoderLayer(nn.Module): def __init__(self, config: AltCLIPConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = AltCLIPAttention(config) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = AltCLIPMLP(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, causal_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 size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. `(config.encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_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 # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->AltCLIP class AltCLIPEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`AltCLIPEncoderLayer`]. Args: config: AltCLIPConfig """ def __init__(self, config: AltCLIPConfig): super().__init__() self.config = config self.layers = nn.ModuleList([AltCLIPEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, causal_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) causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Causal mask for the text model. 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 idx, encoder_layer in enumerate(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, causal_attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, causal_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 ) # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->AltCLIP class AltCLIPVisionEmbeddings(nn.Module): def __init__(self, config: AltCLIPVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: batch_size = pixel_values.shape[0] target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class AltCLIPPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = AltCLIPConfig base_model_prefix = "altclip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" factor = self.config.initializer_factor if isinstance(module, AltCLIPVisionEmbeddings): factor = self.config.initializer_factor nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) elif isinstance(module, AltCLIPAttention): factor = self.config.initializer_factor in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor out_proj_std = (module.embed_dim**-0.5) * factor nn.init.normal_(module.q_proj.weight, std=in_proj_std) nn.init.normal_(module.k_proj.weight, std=in_proj_std) nn.init.normal_(module.v_proj.weight, std=in_proj_std) nn.init.normal_(module.out_proj.weight, std=out_proj_std) elif isinstance(module, AltCLIPMLP): factor = self.config.initializer_factor in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor fc_std = (2 * module.config.hidden_size) ** -0.5 * factor nn.init.normal_(module.fc1.weight, std=fc_std) nn.init.normal_(module.fc2.weight, std=in_proj_std) elif isinstance(module, AltCLIPModel): nn.init.normal_( module.text_projection.weight, std=module.text_embed_dim**-0.5 * self.config.initializer_factor, ) module.text_projection._is_hf_initialized = True nn.init.normal_( module.visual_projection.weight, std=module.vision_embed_dim**-0.5 * self.config.initializer_factor, ) module.visual_projection._is_hf_initialized = True elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) elif isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_factor) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() # Copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer with CLIPVisionTransformer->AltCLIPVisionTransformer,CLIPVisionConfig->AltCLIPVisionConfig,CLIPVisionEmbeddings->AltCLIPVisionEmbeddings,CLIPEncoder->AltCLIPEncoder,CLIP_VISION_INPUTS_DOCSTRING->ALTCLIP_VISION_INPUTS_DOCSTRING class AltCLIPVisionTransformer(nn.Module): def __init__(self, config: AltCLIPVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = AltCLIPVisionEmbeddings(config) self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.encoder = AltCLIPEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) @add_start_docstrings_to_model_forward(ALTCLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = 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 pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(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, ) class AltCLIPVisionModel(AltCLIPPreTrainedModel): config_class = AltCLIPVisionConfig main_input_name = "pixel_values" def __init__(self, config: AltCLIPVisionConfig): super().__init__(config) self.vision_model = AltCLIPVisionTransformer(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(ALTCLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=AltCLIPVisionConfig) def forward( self, pixel_values: Optional[torch.FloatTensor] = 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, AltCLIPVisionModel >>> model = AltCLIPVisionModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> 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 CLS states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class AltRobertaModel(AltCLIPPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 """ config_class = AltCLIPTextConfig # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->AltRoberta def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = AltRobertaEmbeddings(config) self.encoder = AltRobertaEncoder(config) self.pooler = AltRobertaPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) # Copied from transformers.models.bert.modeling_bert.BertModel.forward def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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 = 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 self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class AltCLIPTextModel(AltCLIPPreTrainedModel): config_class = AltCLIPTextConfig def __init__(self, config): super().__init__(config) self.roberta = AltRobertaModel(config, add_pooling_layer=False) self.transformation = nn.Linear(config.hidden_size, config.project_dim) self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.post_init() def get_input_embeddings(self) -> nn.Module: return self.roberta.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Embedding) -> None: self.roberta.embeddings.word_embeddings = value def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: return super().resize_token_embeddings(new_num_tokens) @add_start_docstrings_to_model_forward(ALTCLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndProjection, config_class=AltCLIPTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPoolingAndProjection]: r""" Returns: Examples: ```python >>> from transformers import AutoProcessor, AltCLIPTextModel >>> model = AltCLIPTextModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> texts = ["it's a cat", "it's a dog"] >>> inputs = processor(text=texts, padding=True, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled CLS states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roberta( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) # last module outputs sequence_output = outputs[0] # project every module sequence_output = self.pre_LN(sequence_output) # pooler projection_state = self.transformation(sequence_output) pooler_output = projection_state[:, 0] if not return_dict: return (projection_state, pooler_output) + outputs[2:4] return BaseModelOutputWithPoolingAndProjection( last_hidden_state=projection_state, pooler_output=pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class AltCLIPModel(AltCLIPPreTrainedModel): config_class = AltCLIPConfig def __init__(self, config: AltCLIPConfig): super().__init__(config) if not isinstance(config.vision_config, AltCLIPVisionConfig): raise ValueError( "config.vision_config is expected to be of type AltCLIPVisionConfig but is of type" f" {type(config.vision_config)}." ) if not isinstance(config.text_config, AltCLIPTextConfig): raise ValueError( "config.text_config is expected to be of type AltCLIPTextConfig but is of type" f" {type(config.text_config)}." ) text_config = config.text_config vision_config = config.vision_config self.projection_dim = config.projection_dim self.text_embed_dim = text_config.project_dim self.vision_embed_dim = vision_config.hidden_size self.text_model = AltCLIPTextModel(text_config) self.vision_model = AltCLIPVisionTransformer(vision_config) self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ALTCLIP_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, token_type_ids=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 [`AltCLIPTextModel`]. Examples: ```python >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> inputs = processor(text=["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt") >>> text_features = model.get_text_features(**inputs) ```""" # Use AltCLIP 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, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(ALTCLIP_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 [`AltCLIPVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""" # Use AltCLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] # pooled_output image_features = self.visual_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward(ALTCLIP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=AltCLIPOutput, config_class=AltCLIPConfig) 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, token_type_ids: Optional[torch.Tensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, AltCLIPOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AltCLIPModel >>> model = AltCLIPModel.from_pretrained("BAAI/AltCLIP") >>> processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ```""" # Use AltCLIP 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, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=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, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # 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 logit_scale = self.logit_scale.exp() logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale logits_per_image = logits_per_text.T loss = None if return_loss: loss = clip_loss(logits_per_text) 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 AltCLIPOutput( 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, ) # Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor """ # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. mask = input_ids.ne(padding_idx).int() incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask return incremental_indices.long() + padding_idx

transformers/src/transformers/models/altclip/modeling_altclip.py/0

{ "file_path": "transformers/src/transformers/models/altclip/modeling_altclip.py", "repo_id": "transformers", "token_count": 33182 }

283

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Tokenizer class.""" import importlib import json import os import warnings from collections import OrderedDict from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union from ...configuration_utils import PretrainedConfig from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code from ...tokenization_utils import PreTrainedTokenizer from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE from ...utils import ( cached_file, extract_commit_hash, is_g2p_en_available, is_sentencepiece_available, is_tokenizers_available, logging, ) from ..encoder_decoder import EncoderDecoderConfig from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, AutoConfig, config_class_to_model_type, model_type_to_module_name, replace_list_option_in_docstrings, ) if is_tokenizers_available(): from ...tokenization_utils_fast import PreTrainedTokenizerFast else: PreTrainedTokenizerFast = None logger = logging.get_logger(__name__) if TYPE_CHECKING: # This significantly improves completion suggestion performance when # the transformers package is used with Microsoft's Pylance language server. TOKENIZER_MAPPING_NAMES: OrderedDict[str, Tuple[Optional[str], Optional[str]]] = OrderedDict() else: TOKENIZER_MAPPING_NAMES = OrderedDict( [ ( "albert", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ("align", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bark", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bart", ("BartTokenizer", "BartTokenizerFast")), ( "barthez", ( "BarthezTokenizer" if is_sentencepiece_available() else None, "BarthezTokenizerFast" if is_tokenizers_available() else None, ), ), ("bartpho", ("BartphoTokenizer", None)), ("bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("bert-generation", ("BertGenerationTokenizer" if is_sentencepiece_available() else None, None)), ("bert-japanese", ("BertJapaneseTokenizer", None)), ("bertweet", ("BertweetTokenizer", None)), ( "big_bird", ( "BigBirdTokenizer" if is_sentencepiece_available() else None, "BigBirdTokenizerFast" if is_tokenizers_available() else None, ), ), ("bigbird_pegasus", ("PegasusTokenizer", "PegasusTokenizerFast" if is_tokenizers_available() else None)), ("biogpt", ("BioGptTokenizer", None)), ("blenderbot", ("BlenderbotTokenizer", "BlenderbotTokenizerFast")), ("blenderbot-small", ("BlenderbotSmallTokenizer", None)), ("blip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("blip-2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)), ("bridgetower", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("bros", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("byt5", ("ByT5Tokenizer", None)), ( "camembert", ( "CamembertTokenizer" if is_sentencepiece_available() else None, "CamembertTokenizerFast" if is_tokenizers_available() else None, ), ), ("canine", ("CanineTokenizer", None)), ("chinese_clip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "clap", ( "RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "clip", ( "CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None, ), ), ( "clipseg", ( "CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None, ), ), ("clvp", ("ClvpTokenizer", None)), ( "code_llama", ( "CodeLlamaTokenizer" if is_sentencepiece_available() else None, "CodeLlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("codegen", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)), ("cohere", (None, "CohereTokenizerFast" if is_tokenizers_available() else None)), ("convbert", ("ConvBertTokenizer", "ConvBertTokenizerFast" if is_tokenizers_available() else None)), ( "cpm", ( "CpmTokenizer" if is_sentencepiece_available() else None, "CpmTokenizerFast" if is_tokenizers_available() else None, ), ), ("cpmant", ("CpmAntTokenizer", None)), ("ctrl", ("CTRLTokenizer", None)), ("data2vec-audio", ("Wav2Vec2CTCTokenizer", None)), ("data2vec-text", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("deberta", ("DebertaTokenizer", "DebertaTokenizerFast" if is_tokenizers_available() else None)), ( "deberta-v2", ( "DebertaV2Tokenizer" if is_sentencepiece_available() else None, "DebertaV2TokenizerFast" if is_tokenizers_available() else None, ), ), ("distilbert", ("DistilBertTokenizer", "DistilBertTokenizerFast" if is_tokenizers_available() else None)), ( "dpr", ( "DPRQuestionEncoderTokenizer", "DPRQuestionEncoderTokenizerFast" if is_tokenizers_available() else None, ), ), ("electra", ("ElectraTokenizer", "ElectraTokenizerFast" if is_tokenizers_available() else None)), ("ernie", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("ernie_m", ("ErnieMTokenizer" if is_sentencepiece_available() else None, None)), ("esm", ("EsmTokenizer", None)), ("falcon", (None, "PreTrainedTokenizerFast" if is_tokenizers_available() else None)), ( "fastspeech2_conformer", ("FastSpeech2ConformerTokenizer" if is_g2p_en_available() else None, None), ), ("flaubert", ("FlaubertTokenizer", None)), ("fnet", ("FNetTokenizer", "FNetTokenizerFast" if is_tokenizers_available() else None)), ("fsmt", ("FSMTTokenizer", None)), ("funnel", ("FunnelTokenizer", "FunnelTokenizerFast" if is_tokenizers_available() else None)), ( "gemma", ( "GemmaTokenizer" if is_sentencepiece_available() else None, "GemmaTokenizerFast" if is_tokenizers_available() else None, ), ), ("git", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("gpt-sw3", ("GPTSw3Tokenizer" if is_sentencepiece_available() else None, None)), ("gpt2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_bigcode", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_neo", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gpt_neox", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("gpt_neox_japanese", ("GPTNeoXJapaneseTokenizer", None)), ("gptj", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("gptsan-japanese", ("GPTSanJapaneseTokenizer", None)), ("groupvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ("herbert", ("HerbertTokenizer", "HerbertTokenizerFast" if is_tokenizers_available() else None)), ("hubert", ("Wav2Vec2CTCTokenizer", None)), ("ibert", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("idefics", (None, "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("instructblip", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("jukebox", ("JukeboxTokenizer", None)), ( "kosmos-2", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ("layoutlm", ("LayoutLMTokenizer", "LayoutLMTokenizerFast" if is_tokenizers_available() else None)), ("layoutlmv2", ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast" if is_tokenizers_available() else None)), ("layoutlmv3", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)), ("layoutxlm", ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast" if is_tokenizers_available() else None)), ("led", ("LEDTokenizer", "LEDTokenizerFast" if is_tokenizers_available() else None)), ("lilt", ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast" if is_tokenizers_available() else None)), ( "llama", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("llava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("llava_next", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("longformer", ("LongformerTokenizer", "LongformerTokenizerFast" if is_tokenizers_available() else None)), ( "longt5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("luke", ("LukeTokenizer", None)), ("lxmert", ("LxmertTokenizer", "LxmertTokenizerFast" if is_tokenizers_available() else None)), ("m2m_100", ("M2M100Tokenizer" if is_sentencepiece_available() else None, None)), ("mamba", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("marian", ("MarianTokenizer" if is_sentencepiece_available() else None, None)), ( "mbart", ( "MBartTokenizer" if is_sentencepiece_available() else None, "MBartTokenizerFast" if is_tokenizers_available() else None, ), ), ( "mbart50", ( "MBart50Tokenizer" if is_sentencepiece_available() else None, "MBart50TokenizerFast" if is_tokenizers_available() else None, ), ), ("mega", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("mgp-str", ("MgpstrTokenizer", None)), ( "mistral", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "mixtral", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("mluke", ("MLukeTokenizer" if is_sentencepiece_available() else None, None)), ("mobilebert", ("MobileBertTokenizer", "MobileBertTokenizerFast" if is_tokenizers_available() else None)), ("mpnet", ("MPNetTokenizer", "MPNetTokenizerFast" if is_tokenizers_available() else None)), ("mpt", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("mra", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ( "mt5", ( "MT5Tokenizer" if is_sentencepiece_available() else None, "MT5TokenizerFast" if is_tokenizers_available() else None, ), ), ("musicgen", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)), ("musicgen_melody", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)), ("mvp", ("MvpTokenizer", "MvpTokenizerFast" if is_tokenizers_available() else None)), ("nezha", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "nllb", ( "NllbTokenizer" if is_sentencepiece_available() else None, "NllbTokenizerFast" if is_tokenizers_available() else None, ), ), ( "nllb-moe", ( "NllbTokenizer" if is_sentencepiece_available() else None, "NllbTokenizerFast" if is_tokenizers_available() else None, ), ), ( "nystromformer", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ("oneformer", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None), ), ("opt", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ("owlv2", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ("owlvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "pegasus", ( "PegasusTokenizer" if is_sentencepiece_available() else None, "PegasusTokenizerFast" if is_tokenizers_available() else None, ), ), ( "pegasus_x", ( "PegasusTokenizer" if is_sentencepiece_available() else None, "PegasusTokenizerFast" if is_tokenizers_available() else None, ), ), ( "perceiver", ( "PerceiverTokenizer", None, ), ), ( "persimmon", ( "LlamaTokenizer" if is_sentencepiece_available() else None, "LlamaTokenizerFast" if is_tokenizers_available() else None, ), ), ("phi", ("CodeGenTokenizer", "CodeGenTokenizerFast" if is_tokenizers_available() else None)), ("phobert", ("PhobertTokenizer", None)), ("pix2struct", ("T5Tokenizer", "T5TokenizerFast" if is_tokenizers_available() else None)), ("plbart", ("PLBartTokenizer" if is_sentencepiece_available() else None, None)), ("prophetnet", ("ProphetNetTokenizer", None)), ("qdqbert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "qwen2", ( "Qwen2Tokenizer", "Qwen2TokenizerFast" if is_tokenizers_available() else None, ), ), ("rag", ("RagTokenizer", None)), ("realm", ("RealmTokenizer", "RealmTokenizerFast" if is_tokenizers_available() else None)), ( "reformer", ( "ReformerTokenizer" if is_sentencepiece_available() else None, "ReformerTokenizerFast" if is_tokenizers_available() else None, ), ), ( "rembert", ( "RemBertTokenizer" if is_sentencepiece_available() else None, "RemBertTokenizerFast" if is_tokenizers_available() else None, ), ), ("retribert", ("RetriBertTokenizer", "RetriBertTokenizerFast" if is_tokenizers_available() else None)), ("roberta", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)), ( "roberta-prelayernorm", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None), ), ("roc_bert", ("RoCBertTokenizer", None)), ("roformer", ("RoFormerTokenizer", "RoFormerTokenizerFast" if is_tokenizers_available() else None)), ("rwkv", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ( "seamless_m4t", ( "SeamlessM4TTokenizer" if is_sentencepiece_available() else None, "SeamlessM4TTokenizerFast" if is_tokenizers_available() else None, ), ), ( "seamless_m4t_v2", ( "SeamlessM4TTokenizer" if is_sentencepiece_available() else None, "SeamlessM4TTokenizerFast" if is_tokenizers_available() else None, ), ), ("siglip", ("SiglipTokenizer" if is_sentencepiece_available() else None, None)), ("speech_to_text", ("Speech2TextTokenizer" if is_sentencepiece_available() else None, None)), ("speech_to_text_2", ("Speech2Text2Tokenizer", None)), ("speecht5", ("SpeechT5Tokenizer" if is_sentencepiece_available() else None, None)), ("splinter", ("SplinterTokenizer", "SplinterTokenizerFast")), ( "squeezebert", ("SqueezeBertTokenizer", "SqueezeBertTokenizerFast" if is_tokenizers_available() else None), ), ("stablelm", (None, "GPTNeoXTokenizerFast" if is_tokenizers_available() else None)), ("starcoder2", ("GPT2Tokenizer", "GPT2TokenizerFast" if is_tokenizers_available() else None)), ( "switch_transformers", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ( "t5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("tapas", ("TapasTokenizer", None)), ("tapex", ("TapexTokenizer", None)), ("transfo-xl", ("TransfoXLTokenizer", None)), ("tvp", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ( "udop", ( "UdopTokenizer" if is_sentencepiece_available() else None, "UdopTokenizerFast" if is_tokenizers_available() else None, ), ), ( "umt5", ( "T5Tokenizer" if is_sentencepiece_available() else None, "T5TokenizerFast" if is_tokenizers_available() else None, ), ), ("vilt", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("vipllava", ("LlamaTokenizer", "LlamaTokenizerFast" if is_tokenizers_available() else None)), ("visual_bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)), ("vits", ("VitsTokenizer", None)), ("wav2vec2", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2-bert", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2-conformer", ("Wav2Vec2CTCTokenizer", None)), ("wav2vec2_phoneme", ("Wav2Vec2PhonemeCTCTokenizer", None)), ("whisper", ("WhisperTokenizer", "WhisperTokenizerFast" if is_tokenizers_available() else None)), ("xclip", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)), ( "xglm", ( "XGLMTokenizer" if is_sentencepiece_available() else None, "XGLMTokenizerFast" if is_tokenizers_available() else None, ), ), ("xlm", ("XLMTokenizer", None)), ("xlm-prophetnet", ("XLMProphetNetTokenizer" if is_sentencepiece_available() else None, None)), ( "xlm-roberta", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xlm-roberta-xl", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xlnet", ( "XLNetTokenizer" if is_sentencepiece_available() else None, "XLNetTokenizerFast" if is_tokenizers_available() else None, ), ), ( "xmod", ( "XLMRobertaTokenizer" if is_sentencepiece_available() else None, "XLMRobertaTokenizerFast" if is_tokenizers_available() else None, ), ), ( "yoso", ( "AlbertTokenizer" if is_sentencepiece_available() else None, "AlbertTokenizerFast" if is_tokenizers_available() else None, ), ), ] ) TOKENIZER_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, TOKENIZER_MAPPING_NAMES) CONFIG_TO_TYPE = {v: k for k, v in CONFIG_MAPPING_NAMES.items()} def tokenizer_class_from_name(class_name: str): if class_name == "PreTrainedTokenizerFast": return PreTrainedTokenizerFast for module_name, tokenizers in TOKENIZER_MAPPING_NAMES.items(): if class_name in tokenizers: module_name = model_type_to_module_name(module_name) module = importlib.import_module(f".{module_name}", "transformers.models") try: return getattr(module, class_name) except AttributeError: continue for config, tokenizers in TOKENIZER_MAPPING._extra_content.items(): for tokenizer in tokenizers: if getattr(tokenizer, "__name__", None) == class_name: return tokenizer # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main # init and we return the proper dummy to get an appropriate error message. main_module = importlib.import_module("transformers") if hasattr(main_module, class_name): return getattr(main_module, class_name) return None def get_tokenizer_config( pretrained_model_name_or_path: Union[str, os.PathLike], cache_dir: Optional[Union[str, os.PathLike]] = None, force_download: bool = False, resume_download: bool = False, proxies: Optional[Dict[str, str]] = None, token: Optional[Union[bool, str]] = None, revision: Optional[str] = None, local_files_only: bool = False, subfolder: str = "", **kwargs, ): """ Loads the tokenizer configuration from a pretrained model tokenizer configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. - a path to a *directory* containing a configuration file saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force to (re-)download the configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request. token (`str` or *bool*, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`). revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. local_files_only (`bool`, *optional*, defaults to `False`): If `True`, will only try to load the tokenizer configuration from local files. subfolder (`str`, *optional*, defaults to `""`): In case the tokenizer config is located inside a subfolder of the model repo on huggingface.co, you can specify the folder name here. <Tip> Passing `token=True` is required when you want to use a private model. </Tip> Returns: `Dict`: The configuration of the tokenizer. Examples: ```python # Download configuration from huggingface.co and cache. tokenizer_config = get_tokenizer_config("google-bert/bert-base-uncased") # This model does not have a tokenizer config so the result will be an empty dict. tokenizer_config = get_tokenizer_config("FacebookAI/xlm-roberta-base") # Save a pretrained tokenizer locally and you can reload its config from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") tokenizer.save_pretrained("tokenizer-test") tokenizer_config = get_tokenizer_config("tokenizer-test") ```""" use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if token is not None: raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") token = use_auth_token commit_hash = kwargs.get("_commit_hash", None) resolved_config_file = cached_file( pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE, cache_dir=cache_dir, force_download=force_download, resume_download=resume_download, proxies=proxies, token=token, revision=revision, local_files_only=local_files_only, subfolder=subfolder, _raise_exceptions_for_gated_repo=False, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, _commit_hash=commit_hash, ) if resolved_config_file is None: logger.info("Could not locate the tokenizer configuration file, will try to use the model config instead.") return {} commit_hash = extract_commit_hash(resolved_config_file, commit_hash) with open(resolved_config_file, encoding="utf-8") as reader: result = json.load(reader) result["_commit_hash"] = commit_hash return result class AutoTokenizer: r""" This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the [`AutoTokenizer.from_pretrained`] class method. This class cannot be instantiated directly using `__init__()` (throws an error). """ def __init__(self): raise EnvironmentError( "AutoTokenizer is designed to be instantiated " "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method." ) @classmethod @replace_list_option_in_docstrings(TOKENIZER_MAPPING_NAMES) def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary. The tokenizer class to instantiate is selected based on the `model_type` property of the config object (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: List options Params: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the *model id* of a predefined tokenizer hosted inside a model repo on huggingface.co. - A path to a *directory* containing vocabulary files required by the tokenizer, for instance saved using the [`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`. - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (like Bert or XLNet), e.g.: `./my_model_directory/vocab.txt`. (Not applicable to all derived classes) inputs (additional positional arguments, *optional*): Will be passed along to the Tokenizer `__init__()` method. config ([`PretrainedConfig`], *optional*) The configuration object used to determine the tokenizer class to instantiate. cache_dir (`str` or `os.PathLike`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. resume_download (`bool`, *optional*, defaults to `False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. revision (`str`, *optional*, defaults to `"main"`): The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. subfolder (`str`, *optional*): In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for facebook/rag-token-base), specify it here. use_fast (`bool`, *optional*, defaults to `True`): Use a [fast Rust-based tokenizer](https://huggingface.co/docs/tokenizers/index) if it is supported for a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer is returned instead. tokenizer_type (`str`, *optional*): Tokenizer type to be loaded. trust_remote_code (`bool`, *optional*, defaults to `False`): Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be set to `True` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. kwargs (additional keyword arguments, *optional*): Will be passed to the Tokenizer `__init__()` method. Can be used to set special tokens like `bos_token`, `eos_token`, `unk_token`, `sep_token`, `pad_token`, `cls_token`, `mask_token`, `additional_special_tokens`. See parameters in the `__init__()` for more details. Examples: ```python >>> from transformers import AutoTokenizer >>> # Download vocabulary from huggingface.co and cache. >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> # Download vocabulary from huggingface.co (user-uploaded) and cache. >>> tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased") >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained('./test/saved_model/')*) >>> # tokenizer = AutoTokenizer.from_pretrained("./test/bert_saved_model/") >>> # Download vocabulary from huggingface.co and define model-specific arguments >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base", add_prefix_space=True) ```""" use_auth_token = kwargs.pop("use_auth_token", None) if use_auth_token is not None: warnings.warn( "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.", FutureWarning, ) if kwargs.get("token", None) is not None: raise ValueError( "`token` and `use_auth_token` are both specified. Please set only the argument `token`." ) kwargs["token"] = use_auth_token config = kwargs.pop("config", None) kwargs["_from_auto"] = True use_fast = kwargs.pop("use_fast", True) tokenizer_type = kwargs.pop("tokenizer_type", None) trust_remote_code = kwargs.pop("trust_remote_code", None) # First, let's see whether the tokenizer_type is passed so that we can leverage it if tokenizer_type is not None: tokenizer_class = None tokenizer_class_tuple = TOKENIZER_MAPPING_NAMES.get(tokenizer_type, None) if tokenizer_class_tuple is None: raise ValueError( f"Passed `tokenizer_type` {tokenizer_type} does not exist. `tokenizer_type` should be one of " f"{', '.join(c for c in TOKENIZER_MAPPING_NAMES.keys())}." ) tokenizer_class_name, tokenizer_fast_class_name = tokenizer_class_tuple if use_fast: if tokenizer_fast_class_name is not None: tokenizer_class = tokenizer_class_from_name(tokenizer_fast_class_name) else: logger.warning( "`use_fast` is set to `True` but the tokenizer class does not have a fast version. " " Falling back to the slow version." ) if tokenizer_class is None: tokenizer_class = tokenizer_class_from_name(tokenizer_class_name) if tokenizer_class is None: raise ValueError(f"Tokenizer class {tokenizer_class_name} is not currently imported.") return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # Next, let's try to use the tokenizer_config file to get the tokenizer class. tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs) if "_commit_hash" in tokenizer_config: kwargs["_commit_hash"] = tokenizer_config["_commit_hash"] config_tokenizer_class = tokenizer_config.get("tokenizer_class") tokenizer_auto_map = None if "auto_map" in tokenizer_config: if isinstance(tokenizer_config["auto_map"], (tuple, list)): # Legacy format for dynamic tokenizers tokenizer_auto_map = tokenizer_config["auto_map"] else: tokenizer_auto_map = tokenizer_config["auto_map"].get("AutoTokenizer", None) # If that did not work, let's try to use the config. if config_tokenizer_class is None: if not isinstance(config, PretrainedConfig): config = AutoConfig.from_pretrained( pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs ) config_tokenizer_class = config.tokenizer_class if hasattr(config, "auto_map") and "AutoTokenizer" in config.auto_map: tokenizer_auto_map = config.auto_map["AutoTokenizer"] has_remote_code = tokenizer_auto_map is not None has_local_code = type(config) in TOKENIZER_MAPPING or ( config_tokenizer_class is not None and ( tokenizer_class_from_name(config_tokenizer_class) is not None or tokenizer_class_from_name(config_tokenizer_class + "Fast") is not None ) ) trust_remote_code = resolve_trust_remote_code( trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code ) if has_remote_code and trust_remote_code: if use_fast and tokenizer_auto_map[1] is not None: class_ref = tokenizer_auto_map[1] else: class_ref = tokenizer_auto_map[0] tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs) _ = kwargs.pop("code_revision", None) if os.path.isdir(pretrained_model_name_or_path): tokenizer_class.register_for_auto_class() return tokenizer_class.from_pretrained( pretrained_model_name_or_path, *inputs, trust_remote_code=trust_remote_code, **kwargs ) elif config_tokenizer_class is not None: tokenizer_class = None if use_fast and not config_tokenizer_class.endswith("Fast"): tokenizer_class_candidate = f"{config_tokenizer_class}Fast" tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate) if tokenizer_class is None: tokenizer_class_candidate = config_tokenizer_class tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate) if tokenizer_class is None: raise ValueError( f"Tokenizer class {tokenizer_class_candidate} does not exist or is not currently imported." ) return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # Otherwise we have to be creative. # if model is an encoder decoder, the encoder tokenizer class is used by default if isinstance(config, EncoderDecoderConfig): if type(config.decoder) is not type(config.encoder): # noqa: E721 logger.warning( f"The encoder model config class: {config.encoder.__class__} is different from the decoder model " f"config class: {config.decoder.__class__}. It is not recommended to use the " "`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder " "specific tokenizer classes." ) config = config.encoder model_type = config_class_to_model_type(type(config).__name__) if model_type is not None: tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)] if tokenizer_class_fast and (use_fast or tokenizer_class_py is None): return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: if tokenizer_class_py is not None: return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: raise ValueError( "This tokenizer cannot be instantiated. Please make sure you have `sentencepiece` installed " "in order to use this tokenizer." ) raise ValueError( f"Unrecognized configuration class {config.__class__} to build an AutoTokenizer.\n" f"Model type should be one of {', '.join(c.__name__ for c in TOKENIZER_MAPPING.keys())}." ) def register(config_class, slow_tokenizer_class=None, fast_tokenizer_class=None, exist_ok=False): """ Register a new tokenizer in this mapping. Args: config_class ([`PretrainedConfig`]): The configuration corresponding to the model to register. slow_tokenizer_class ([`PretrainedTokenizer`], *optional*): The slow tokenizer to register. fast_tokenizer_class ([`PretrainedTokenizerFast`], *optional*): The fast tokenizer to register. """ if slow_tokenizer_class is None and fast_tokenizer_class is None: raise ValueError("You need to pass either a `slow_tokenizer_class` or a `fast_tokenizer_class") if slow_tokenizer_class is not None and issubclass(slow_tokenizer_class, PreTrainedTokenizerFast): raise ValueError("You passed a fast tokenizer in the `slow_tokenizer_class`.") if fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PreTrainedTokenizer): raise ValueError("You passed a slow tokenizer in the `fast_tokenizer_class`.") if ( slow_tokenizer_class is not None and fast_tokenizer_class is not None and issubclass(fast_tokenizer_class, PreTrainedTokenizerFast) and fast_tokenizer_class.slow_tokenizer_class != slow_tokenizer_class ): raise ValueError( "The fast tokenizer class you are passing has a `slow_tokenizer_class` attribute that is not " "consistent with the slow tokenizer class you passed (fast tokenizer has " f"{fast_tokenizer_class.slow_tokenizer_class} and you passed {slow_tokenizer_class}. Fix one of those " "so they match!" ) # Avoid resetting a set slow/fast tokenizer if we are passing just the other ones. if config_class in TOKENIZER_MAPPING._extra_content: existing_slow, existing_fast = TOKENIZER_MAPPING[config_class] if slow_tokenizer_class is None: slow_tokenizer_class = existing_slow if fast_tokenizer_class is None: fast_tokenizer_class = existing_fast TOKENIZER_MAPPING.register(config_class, (slow_tokenizer_class, fast_tokenizer_class), exist_ok=exist_ok)

transformers/src/transformers/models/auto/tokenization_auto.py/0

{ "file_path": "transformers/src/transformers/models/auto/tokenization_auto.py", "repo_id": "transformers", "token_count": 21073 }

284

# Copyright 2020 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. """ This script can be used to convert a head-less TF2.x Bert model to PyTorch, as published on the official (now deprecated) GitHub: https://github.com/tensorflow/models/tree/v2.3.0/official/nlp/bert TF2.x uses different variable names from the original BERT (TF 1.4) implementation. The script re-maps the TF2.x Bert weight names to the original names, so the model can be imported with Huggingface/transformer. You may adapt this script to include classification/MLM/NSP/etc. heads. Note: This script is only working with an older version of the TensorFlow models repository (<= v2.3.0). Models trained with never versions are not compatible with this script. """ import argparse import os import re import tensorflow as tf import torch from transformers import BertConfig, BertModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_tf2_weights_in_bert(model, tf_checkpoint_path, config): tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] layer_depth = [] for full_name, shape in init_vars: # logger.info(f"Loading TF weight {name} with shape {shape}") name = full_name.split("/") if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]: logger.info(f"Skipping non-model layer {full_name}") continue if "optimizer" in full_name: logger.info(f"Skipping optimization layer {full_name}") continue if name[0] == "model": # ignore initial 'model' name = name[1:] # figure out how many levels deep the name is depth = 0 for _name in name: if _name.startswith("layer_with_weights"): depth += 1 else: break layer_depth.append(depth) # read data array = tf.train.load_variable(tf_path, full_name) names.append("/".join(name)) arrays.append(array) logger.info(f"Read a total of {len(arrays):,} layers") # Sanity check if len(set(layer_depth)) != 1: raise ValueError(f"Found layer names with different depths (layer depth {list(set(layer_depth))})") layer_depth = list(set(layer_depth))[0] if layer_depth != 1: raise ValueError( "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP" " heads." ) # convert layers logger.info("Converting weights...") for full_name, array in zip(names, arrays): name = full_name.split("/") pointer = model trace = [] for i, m_name in enumerate(name): if m_name == ".ATTRIBUTES": # variable names end with .ATTRIBUTES/VARIABLE_VALUE break if m_name.startswith("layer_with_weights"): layer_num = int(m_name.split("-")[-1]) if layer_num <= 2: # embedding layers # layer_num 0: word_embeddings # layer_num 1: position_embeddings # layer_num 2: token_type_embeddings continue elif layer_num == 3: # embedding LayerNorm trace.extend(["embeddings", "LayerNorm"]) pointer = getattr(pointer, "embeddings") pointer = getattr(pointer, "LayerNorm") elif layer_num > 3 and layer_num < config.num_hidden_layers + 4: # encoder layers trace.extend(["encoder", "layer", str(layer_num - 4)]) pointer = getattr(pointer, "encoder") pointer = getattr(pointer, "layer") pointer = pointer[layer_num - 4] elif layer_num == config.num_hidden_layers + 4: # pooler layer trace.extend(["pooler", "dense"]) pointer = getattr(pointer, "pooler") pointer = getattr(pointer, "dense") elif m_name == "embeddings": trace.append("embeddings") pointer = getattr(pointer, "embeddings") if layer_num == 0: trace.append("word_embeddings") pointer = getattr(pointer, "word_embeddings") elif layer_num == 1: trace.append("position_embeddings") pointer = getattr(pointer, "position_embeddings") elif layer_num == 2: trace.append("token_type_embeddings") pointer = getattr(pointer, "token_type_embeddings") else: raise ValueError(f"Unknown embedding layer with name {full_name}") trace.append("weight") pointer = getattr(pointer, "weight") elif m_name == "_attention_layer": # self-attention layer trace.extend(["attention", "self"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "self") elif m_name == "_attention_layer_norm": # output attention norm trace.extend(["attention", "output", "LayerNorm"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_attention_output_dense": # output attention dense trace.extend(["attention", "output", "dense"]) pointer = getattr(pointer, "attention") pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_dense": # output dense trace.extend(["output", "dense"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output dense trace.extend(["output", "LayerNorm"]) pointer = getattr(pointer, "output") pointer = getattr(pointer, "LayerNorm") elif m_name == "_key_dense": # attention key trace.append("key") pointer = getattr(pointer, "key") elif m_name == "_query_dense": # attention query trace.append("query") pointer = getattr(pointer, "query") elif m_name == "_value_dense": # attention value trace.append("value") pointer = getattr(pointer, "value") elif m_name == "_intermediate_dense": # attention intermediate dense trace.extend(["intermediate", "dense"]) pointer = getattr(pointer, "intermediate") pointer = getattr(pointer, "dense") elif m_name == "_output_layer_norm": # output layer norm trace.append("output") pointer = getattr(pointer, "output") # weights & biases elif m_name in ["bias", "beta"]: trace.append("bias") pointer = getattr(pointer, "bias") elif m_name in ["kernel", "gamma"]: trace.append("weight") pointer = getattr(pointer, "weight") else: logger.warning(f"Ignored {m_name}") # for certain layers reshape is necessary trace = ".".join(trace) if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)", trace) or re.match( r"(\S+)\.attention\.output\.dense\.weight", trace ): array = array.reshape(pointer.data.shape) if "kernel" in full_name: array = array.transpose() if pointer.shape == array.shape: pointer.data = torch.from_numpy(array) else: raise ValueError( f"Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:" f" {array.shape}" ) logger.info(f"Successfully set variable {full_name} to PyTorch layer {trace}") return model def convert_tf2_checkpoint_to_pytorch(tf_checkpoint_path, config_path, pytorch_dump_path): # Instantiate model logger.info(f"Loading model based on config from {config_path}...") config = BertConfig.from_json_file(config_path) model = BertModel(config) # Load weights from checkpoint logger.info(f"Loading weights from checkpoint {tf_checkpoint_path}...") load_tf2_weights_in_bert(model, tf_checkpoint_path, config) # Save pytorch-model logger.info(f"Saving PyTorch model to {pytorch_dump_path}...") torch.save(model.state_dict(), pytorch_dump_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x checkpoint path." ) parser.add_argument( "--bert_config_file", type=str, required=True, help="The config json file corresponding to the BERT model. This specifies the model architecture.", ) parser.add_argument( "--pytorch_dump_path", type=str, required=True, help="Path to the output PyTorch model (must include filename).", ) args = parser.parse_args() convert_tf2_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 4807 }

285

# coding=utf-8 # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science 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 BioGPT model.""" import math from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, ) from .configuration_biogpt import BioGptConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "microsoft/biogpt" _CONFIG_FOR_DOC = "BioGptConfig" BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/biogpt", "microsoft/BioGPT-Large", # See all BioGPT models at https://huggingface.co/models?filter=biogpt ] # Copied from transformers.models.opt.modeling_opt.OPTLearnedPositionalEmbedding with OPT->BioGpt class BioGptLearnedPositionalEmbedding(nn.Embedding): """ This module learns positional embeddings up to a fixed maximum size. """ def __init__(self, num_embeddings: int, embedding_dim: int): # BioGpt is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 super().__init__(num_embeddings + self.offset, embedding_dim) def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0): """`input_ids_shape` is expected to be [bsz x seqlen].""" attention_mask = attention_mask.long() # create positions depending on attention_mask positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 # cut positions if `past_key_values_length` is > 0 positions = positions[:, past_key_values_length:] return super().forward(positions + self.offset) # Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->BioGpt class BioGptAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, embed_dim: int, num_heads: int, dropout: float = 0.0, is_decoder: bool = False, bias: bool = True, is_causal: bool = False, config: Optional[BioGptConfig] = None, ): super().__init__() self.embed_dim = embed_dim self.num_heads = num_heads self.dropout = dropout self.head_dim = embed_dim // num_heads self.config = config if (self.head_dim * num_heads) != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {num_heads})." ) self.scaling = self.head_dim**-0.5 self.is_decoder = is_decoder self.is_causal = is_causal self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() def forward( self, hidden_states: torch.Tensor, key_value_states: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None bsz, tgt_len, _ = hidden_states.size() # get query proj query_states = self.q_proj(hidden_states) * self.scaling # get key, value proj # `past_key_value[0].shape[2] == key_value_states.shape[1]` # is checking that the `sequence_length` of the `past_key_value` is the same as # the provided `key_value_states` to support prefix tuning if ( is_cross_attention and past_key_value is not None and past_key_value[0].shape[2] == key_value_states.shape[1] ): # reuse k,v, cross_attentions key_states = past_key_value[0] value_states = past_key_value[1] elif is_cross_attention: # cross_attentions key_states = self._shape(self.k_proj(key_value_states), -1, bsz) value_states = self._shape(self.v_proj(key_value_states), -1, bsz) elif past_key_value is not None: # reuse k, v, self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) else: # self_attention key_states = self._shape(self.k_proj(hidden_states), -1, bsz) value_states = self._shape(self.v_proj(hidden_states), -1, bsz) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_states, value_states) proj_shape = (bsz * self.num_heads, -1, self.head_dim) query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) key_states = key_states.reshape(*proj_shape) value_states = value_states.reshape(*proj_shape) src_len = key_states.size(1) attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): raise ValueError( f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (bsz, 1, tgt_len, src_len): raise ValueError( f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) attn_weights = nn.functional.softmax(attn_weights, dim=-1) if layer_head_mask is not None: if layer_head_mask.size() != (self.num_heads,): raise ValueError( f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" f" {layer_head_mask.size()}" ) attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) if output_attentions: # this operation is a bit awkward, but it's required to # make sure that attn_weights keeps its gradient. # In order to do so, attn_weights have to be reshaped # twice and have to be reused in the following attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) else: attn_weights_reshaped = None attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.bmm(attn_probs, value_states) if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) attn_output = attn_output.transpose(1, 2) # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be # partitioned across GPUs when using tensor-parallelism. attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights_reshaped, past_key_value class BioGptDecoderLayer(nn.Module): def __init__(self, config: BioGptConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = BioGptAttention( embed_dim=self.embed_dim, num_heads=config.num_attention_heads, dropout=config.attention_probs_dropout_prob, is_decoder=True, ) self.dropout = config.hidden_dropout_prob self.activation_fn = ACT2FN[config.hidden_act] self.activation_dropout = config.activation_dropout self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) self.fc1 = nn.Linear(self.embed_dim, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, self.embed_dim) self.final_layer_norm = nn.LayerNorm(self.embed_dim) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, layer_head_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = True, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """ Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. 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`). """ residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None # add present self-attn cache to positions 1,2 of present_key_value tuple hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, past_key_value=self_attn_past_key_value, attention_mask=attention_mask, layer_head_mask=layer_head_mask, output_attentions=output_attentions, ) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) hidden_states = self.fc2(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class BioGptPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = BioGptConfig base_model_prefix = "biogpt" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) BIOGPT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`~BioGptConfig`]): 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. """ BIOGPT_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, 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. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare BioGPT Model transformer outputting raw hidden-states without any specific head on top.", BIOGPT_START_DOCSTRING, ) class BioGptModel(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.config = config self.layerdrop = config.layerdrop self.dropout = config.hidden_dropout_prob self.embed_dim = config.hidden_size self.padding_idx = config.pad_token_id self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 self.embed_tokens = nn.Embedding(config.vocab_size, self.embed_dim, self.padding_idx) self.embed_positions = BioGptLearnedPositionalEmbedding(config.max_position_embeddings, self.embed_dim) self.layers = nn.ModuleList([BioGptDecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.layer_norm = nn.LayerNorm(self.embed_dim) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_tokens def set_input_embeddings(self, value): self.embed_tokens = value @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input = input_ids input_shape = input.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] input = inputs_embeds[:, :, -1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if inputs_embeds is None: inputs_embeds = self.embed_tokens(input) * self.embed_scale if attention_mask is None: attention_mask = torch.ones( (inputs_embeds.shape[0], inputs_embeds.shape[1] + past_key_values_length), dtype=torch.bool, device=inputs_embeds.device, ) elif attention_mask.shape[1] != past_key_values_length + input_shape[1]: raise ValueError( f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be " f"{past_key_values_length + input_shape[1]} (sum of the lengths of current and past inputs)" ) # embed positions positions = self.embed_positions(attention_mask, past_key_values_length) attention_mask = _prepare_4d_causal_attention_mask( attention_mask, input_shape, inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds + positions hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = None next_decoder_cache = () if use_cache else None for idx, decoder_layer in enumerate(self.layers): # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) if output_hidden_states: all_hidden_states += (hidden_states,) if self.training: dropout_probability = torch.rand([]) if dropout_probability < self.layerdrop: continue past_key_value = past_key_values[idx] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, attention_mask, head_mask[idx] if head_mask is not None else None, None, output_attentions, use_cache, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, layer_head_mask=(head_mask[idx] if head_mask is not None else None), past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[2 if output_attentions else 1],) if output_attentions: all_self_attns += (layer_outputs[1],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) hidden_states = self.layer_norm(hidden_states) next_cache = next_decoder_cache if use_cache else None if not return_dict: return tuple( v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) @add_start_docstrings( """BioGPT Model with a `language modeling` head on top for CLM fine-tuning.""", BIOGPT_START_DOCSTRING ) class BioGptForCausalLM(BioGptPreTrainedModel): _tied_weights_keys = ["output_projection.weight"] def __init__(self, config): super().__init__(config) self.biogpt = BioGptModel(config) self.output_projection = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.output_projection def set_output_embeddings(self, new_embeddings): self.output_projection = new_embeddings @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.biogpt( input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.output_projection(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation( self, input_ids, attention_mask, inputs_embeds=None, past_key_values=None, **kwargs ): # only last tokens for inputs_ids if past is defined in kwargs if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "attention_mask": attention_mask, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), } ) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), ) return reordered_past @add_start_docstrings( """ BioGPT Model 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. """, BIOGPT_START_DOCSTRING, ) class BioGptForTokenClassification(BioGptPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: classifier_dropout = config.classifier_dropout else: classifier_dropout = config.hidden_dropout_prob self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_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, token_type_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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, 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 transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_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 = transformer_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The BioGpt Model transformer with a sequence classification head on top (linear layer). [`BioGptForSequenceClassification`] 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 is required 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). """, BIOGPT_START_DOCSTRING, ) class BioGptForSequenceClassification(BioGptPreTrainedModel): def __init__(self, config: BioGptConfig): super().__init__(config) self.num_labels = config.num_labels self.biogpt = BioGptModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BIOGPT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = 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 transformer_outputs = self.biogpt( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_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 = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] if self.config.pad_token_id is None: sequence_length = -1 else: if input_ids is not None: sequence_length = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device) else: sequence_length = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_length] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def get_input_embeddings(self): return self.biogpt.embed_tokens def set_input_embeddings(self, value): self.biogpt.embed_tokens = value

transformers/src/transformers/models/biogpt/modeling_biogpt.py/0

{ "file_path": "transformers/src/transformers/models/biogpt/modeling_biogpt.py", "repo_id": "transformers", "token_count": 17918 }

286

# coding=utf-8 # Copyright 2023 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. """ BLIP-2 model configuration""" import os from typing import Union from ...configuration_utils import PretrainedConfig from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from ...utils import logging from ..auto import CONFIG_MAPPING logger = logging.get_logger(__name__) BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json", } class Blip2VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) 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 1408): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 6144): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 39): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries and values in the self-attention layers. Example: ```python >>> from transformers import Blip2VisionConfig, Blip2VisionModel >>> # Initializing a Blip2VisionConfig with Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2VisionConfig() >>> # Initializing a Blip2VisionModel (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "blip_2_vision_model" def __init__( self, hidden_size=1408, intermediate_size=6144, num_hidden_layers=39, num_attention_heads=16, image_size=224, patch_size=14, hidden_act="gelu", layer_norm_eps=1e-6, attention_dropout=0.0, initializer_range=1e-10, qkv_bias=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.patch_size = patch_size self.image_size = image_size self.initializer_range = initializer_range self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.qkv_bias = qkv_bias @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 Blip2Config if config_dict.get("model_type") == "blip-2": 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 Blip2QFormerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a BLIP-2 Querying Transformer (Q-Former) model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Note that [`Blip2QFormerModel`] is very similar to [`BertLMHeadModel`] with interleaved cross-attention. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the Q-Former model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling the model. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. 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. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): 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). initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). cross_attention_frequency (`int`, *optional*, defaults to 2): The frequency of adding cross-attention to the Transformer layers. encoder_hidden_size (`int`, *optional*, defaults to 1408): The hidden size of the hidden states for cross-attention. Examples: ```python >>> from transformers import Blip2QFormerConfig, Blip2QFormerModel >>> # Initializing a BLIP-2 Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2QFormerConfig() >>> # Initializing a model (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2QFormerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "blip_2_qformer" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", cross_attention_frequency=2, encoder_hidden_size=1408, **kwargs, ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.position_embedding_type = position_embedding_type self.cross_attention_frequency = cross_attention_frequency self.encoder_hidden_size = encoder_hidden_size @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 qformer config dict if we are loading from Blip2Config if config_dict.get("model_type") == "blip-2": config_dict = config_dict["qformer_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 Blip2Config(PretrainedConfig): r""" [`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model and language model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the BLIP-2 [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Blip2VisionConfig`]. qformer_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`Blip2QFormerConfig`]. text_config (`dict`, *optional*): Dictionary of configuration options used to initialize any [`PretrainedConfig`]. num_query_tokens (`int`, *optional*, defaults to 32): The number of query tokens passed through the Transformer. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import ( ... Blip2VisionConfig, ... Blip2QFormerConfig, ... OPTConfig, ... Blip2Config, ... Blip2ForConditionalGeneration, ... ) >>> # Initializing a Blip2Config with Salesforce/blip2-opt-2.7b style configuration >>> configuration = Blip2Config() >>> # Initializing a Blip2ForConditionalGeneration (with random weights) from the Salesforce/blip2-opt-2.7b style configuration >>> model = Blip2ForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a Blip2Config from a Blip2VisionConfig, Blip2QFormerConfig and any PretrainedConfig >>> # Initializing BLIP-2 vision, BLIP-2 Q-Former and language model configurations >>> vision_config = Blip2VisionConfig() >>> qformer_config = Blip2QFormerConfig() >>> text_config = OPTConfig() >>> config = Blip2Config.from_text_vision_configs(vision_config, qformer_config, text_config) ```""" model_type = "blip-2" def __init__(self, vision_config=None, qformer_config=None, text_config=None, num_query_tokens=32, **kwargs): super().__init__(**kwargs) if vision_config is None: vision_config = {} logger.info("vision_config is None. initializing the Blip2VisionConfig with default values.") if qformer_config is None: qformer_config = {} logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values.") if text_config is None: text_config = {} logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`).") self.vision_config = Blip2VisionConfig(**vision_config) self.qformer_config = Blip2QFormerConfig(**qformer_config) text_model_type = text_config["model_type"] if "model_type" in text_config else "opt" self.text_config = CONFIG_MAPPING[text_model_type](**text_config) self.tie_word_embeddings = self.text_config.tie_word_embeddings self.is_encoder_decoder = self.text_config.is_encoder_decoder self.num_query_tokens = num_query_tokens self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size self.use_decoder_only_language_model = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES self.initializer_factor = 1.0 self.initializer_range = 0.02 @classmethod def from_vision_qformer_text_configs( cls, vision_config: Blip2VisionConfig, qformer_config: Blip2QFormerConfig, text_config: PretrainedConfig, **kwargs, ): r""" Instantiate a [`Blip2Config`] (or a derived class) from a BLIP-2 vision model, Q-Former and language model configurations. Returns: [`Blip2Config`]: An instance of a configuration object """ return cls( vision_config=vision_config.to_dict(), qformer_config=qformer_config.to_dict(), text_config=text_config.to_dict(), **kwargs, )

transformers/src/transformers/models/blip_2/configuration_blip_2.py/0

{ "file_path": "transformers/src/transformers/models/blip_2/configuration_blip_2.py", "repo_id": "transformers", "token_count": 6313 }

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# coding=utf-8 # Copyright 2023-present NAVER Corp, The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Bros model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) BROS_PRETRAINED_CONFIG_ARCHIVE_MAP = { "jinho8345/bros-base-uncased": "https://huggingface.co/jinho8345/bros-base-uncased/blob/main/config.json", "jinho8345/bros-large-uncased": "https://huggingface.co/jinho8345/bros-large-uncased/blob/main/config.json", } class BrosConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to instantiate a Bros model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Bros [jinho8345/bros-base-uncased](https://huggingface.co/jinho8345/bros-base-uncased) 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 30522): Vocabulary size of the Bros model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`BrosModel`] or [`TFBrosModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. 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. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): 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). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`BrosModel`] or [`TFBrosModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. pad_token_id (`int`, *optional*, defaults to 0): The index of the padding token in the token vocabulary. dim_bbox (`int`, *optional*, defaults to 8): The dimension of the bounding box coordinates. (x0, y1, x1, y0, x1, y1, x0, y1) bbox_scale (`float`, *optional*, defaults to 100.0): The scale factor of the bounding box coordinates. n_relations (`int`, *optional*, defaults to 1): The number of relations for SpadeEE(entity extraction), SpadeEL(entity linking) head. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the classifier head. Examples: ```python >>> from transformers import BrosConfig, BrosModel >>> # Initializing a BROS jinho8345/bros-base-uncased style configuration >>> configuration = BrosConfig() >>> # Initializing a model from the jinho8345/bros-base-uncased style configuration >>> model = BrosModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "bros" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, dim_bbox=8, bbox_scale=100.0, n_relations=1, classifier_dropout_prob=0.1, **kwargs, ): super().__init__( vocab_size=vocab_size, hidden_size=hidden_size, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_dropout_prob=hidden_dropout_prob, attention_probs_dropout_prob=attention_probs_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, initializer_range=initializer_range, layer_norm_eps=layer_norm_eps, pad_token_id=pad_token_id, **kwargs, ) self.dim_bbox = dim_bbox self.bbox_scale = bbox_scale self.n_relations = n_relations self.dim_bbox_sinusoid_emb_2d = self.hidden_size // 4 self.dim_bbox_sinusoid_emb_1d = self.dim_bbox_sinusoid_emb_2d // self.dim_bbox self.dim_bbox_projection = self.hidden_size // self.num_attention_heads self.classifier_dropout_prob = classifier_dropout_prob

transformers/src/transformers/models/bros/configuration_bros.py/0

{ "file_path": "transformers/src/transformers/models/bros/configuration_bros.py", "repo_id": "transformers", "token_count": 2599 }

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# coding=utf-8 # Copyright 2021 Google AI The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch CANINE model.""" import copy import math import os from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_canine import CanineConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/canine-s" _CONFIG_FOR_DOC = "CanineConfig" CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/canine-s", "google/canine-r", # See all CANINE models at https://huggingface.co/models?filter=canine ] # Support up to 16 hash functions. _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223] @dataclass class CanineModelOutputWithPooling(ModelOutput): """ Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow Transformer encoders. Args: 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 (i.e. the output of the final shallow Transformer encoder). pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Hidden-state of the first token of the sequence (classification token) at the last layer of the deep Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. 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 input to each encoder + one for the output of each layer of each encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length // config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial input to each Transformer encoder. The hidden states of the shallow encoders have length `sequence_length`, but the hidden states of the deep encoder have length `sequence_length` // `config.downsampling_rate`. 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 the 3 Transformer encoders of shape `(batch_size, num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length // config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None def load_tf_weights_in_canine(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model # also discard the cls weights (which were used for the next sentence prediction pre-training task) if any( n in [ "adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights", ] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue # if first scope name starts with "bert", change it to "encoder" if name[0] == "bert": name[0] = "encoder" # remove "embeddings" middle name of HashBucketCodepointEmbedders elif name[1] == "embeddings": name.remove(name[1]) # rename segment_embeddings to token_type_embeddings elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings" # rename initial convolutional projection layer elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:] # rename final convolutional projection layer elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:] pointer = model for m_name in name: if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class CanineEmbeddings(nn.Module): """Construct the character, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.config = config # character embeddings shard_embedding_size = config.hidden_size // config.num_hash_functions for i in range(config.num_hash_functions): name = f"HashBucketCodepointEmbedder_{i}" setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size)) self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # 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 ) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int): """ Converts ids to hash bucket ids via multiple hashing. Args: input_ids: The codepoints or other IDs to be hashed. num_hashes: The number of hash functions to use. num_buckets: The number of hash buckets (i.e. embeddings in each table). Returns: A list of tensors, each of which is the hash bucket IDs from one hash function. """ if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}") primes = _PRIMES[:num_hashes] result_tensors = [] for prime in primes: hashed = ((input_ids + 1) * prime) % num_buckets result_tensors.append(hashed) return result_tensors def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int): """Converts IDs (e.g. codepoints) into embeddings via multiple hashing.""" if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0") hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets) embedding_shards = [] for i, hash_bucket_ids in enumerate(hash_bucket_tensors): name = f"HashBucketCodepointEmbedder_{i}" shard_embeddings = getattr(self, name)(hash_bucket_ids) embedding_shards.append(shard_embeddings) return torch.cat(embedding_shards, dim=-1) def forward( self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets( input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets ) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.char_position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class CharactersToMolecules(nn.Module): """Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions.""" def __init__(self, config): super().__init__() self.conv = nn.Conv1d( in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, char_encoding: torch.Tensor) -> torch.Tensor: # `cls_encoding`: [batch, 1, hidden_size] cls_encoding = char_encoding[:, 0:1, :] # char_encoding has shape [batch, char_seq, hidden_size] # We transpose it to be [batch, hidden_size, char_seq] char_encoding = torch.transpose(char_encoding, 1, 2) downsampled = self.conv(char_encoding) downsampled = torch.transpose(downsampled, 1, 2) downsampled = self.activation(downsampled) # Truncate the last molecule in order to reserve a position for [CLS]. # Often, the last position is never used (unless we completely fill the # text buffer). This is important in order to maintain alignment on TPUs # (i.e. a multiple of 128). downsampled_truncated = downsampled[:, 0:-1, :] # We also keep [CLS] as a separate sequence position since we always # want to reserve a position (and the model capacity that goes along # with that) in the deep BERT stack. # `result`: [batch, molecule_seq, molecule_dim] result = torch.cat([cls_encoding, downsampled_truncated], dim=1) result = self.LayerNorm(result) return result class ConvProjection(nn.Module): """ Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size characters. """ def __init__(self, config): super().__init__() self.config = config self.conv = nn.Conv1d( in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1, ) self.activation = ACT2FN[config.hidden_act] # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None, ) -> torch.Tensor: # inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final] # we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq] inputs = torch.transpose(inputs, 1, 2) # PyTorch < 1.9 does not support padding="same" (which is used in the original implementation), # so we pad the tensor manually before passing it to the conv layer # based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38 pad_total = self.config.upsampling_kernel_size - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg pad = nn.ConstantPad1d((pad_beg, pad_end), 0) # `result`: shape (batch_size, char_seq_len, hidden_size) result = self.conv(pad(inputs)) result = torch.transpose(result, 1, 2) result = self.activation(result) result = self.LayerNorm(result) result = self.dropout(result) final_char_seq = result if final_seq_char_positions is not None: # Limit transformer query seq and attention mask to these character # positions to greatly reduce the compute cost. Typically, this is just # done for the MLM training task. # TODO add support for MLM raise NotImplementedError("CanineForMaskedLM is currently not supported") else: query_seq = final_char_seq return query_seq class CanineSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": self.max_position_embeddings = config.max_position_embeddings self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: mixed_query_layer = self.query(from_tensor) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. key_layer = self.transpose_for_scores(self.key(to_tensor)) value_layer = self.transpose_for_scores(self.value(to_tensor)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": seq_length = from_tensor.size()[1] position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1) position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1) distance = position_ids_l - position_ids_r positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility if self.position_embedding_type == "relative_key": relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores elif self.position_embedding_type == "relative_key_query": relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: if attention_mask.ndim == 3: # if attention_mask is 3D, do the following: attention_mask = torch.unsqueeze(attention_mask, dim=1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and the dtype's smallest value for masked positions. attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min # Apply the attention mask (precomputed for all layers in CanineModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class CanineSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward( self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineAttention(nn.Module): """ Additional arguments related to local attention: - **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention. - **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to attend to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`, *optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `from_tensor`. - **attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in *to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to skip when moving to the next block in `to_tensor`. """ def __init__( self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128, attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128, ): super().__init__() self.self = CanineSelfAttention(config) self.output = CanineSelfOutput(config) self.pruned_heads = set() # additional arguments related to local attention self.local = local if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError( "`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped." ) if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError( "`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped." ) self.always_attend_to_first_position = always_attend_to_first_position self.first_position_attends_to_all = first_position_attends_to_all self.attend_from_chunk_width = attend_from_chunk_width self.attend_from_chunk_stride = attend_from_chunk_stride self.attend_to_chunk_width = attend_to_chunk_width self.attend_to_chunk_stride = attend_to_chunk_stride def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: if not self.local: self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions) attention_output = self_outputs[0] else: from_seq_length = to_seq_length = hidden_states.shape[1] from_tensor = to_tensor = hidden_states # Create chunks (windows) that we will attend *from* and then concatenate them. from_chunks = [] if self.first_position_attends_to_all: from_chunks.append((0, 1)) # We must skip this first position so that our output sequence is the # correct length (this matters in the *from* sequence only). from_start = 1 else: from_start = 0 for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride): chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width) from_chunks.append((chunk_start, chunk_end)) # Determine the chunks (windows) that will will attend *to*. to_chunks = [] if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length)) for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride): chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width) to_chunks.append((chunk_start, chunk_end)) if len(from_chunks) != len(to_chunks): raise ValueError( f"Expected to have same number of `from_chunks` ({from_chunks}) and " f"`to_chunks` ({from_chunks}). Check strides." ) # next, compute attention scores for each pair of windows and concatenate attention_output_chunks = [] attention_probs_chunks = [] for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks): from_tensor_chunk = from_tensor[:, from_start:from_end, :] to_tensor_chunk = to_tensor[:, to_start:to_end, :] # `attention_mask`: <float>[batch_size, from_seq, to_seq] # `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk] attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end] if self.always_attend_to_first_position: cls_attention_mask = attention_mask[:, from_start:from_end, 0:1] attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2) cls_position = to_tensor[:, 0:1, :] to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1) attention_outputs_chunk = self.self( from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions ) attention_output_chunks.append(attention_outputs_chunk[0]) if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1]) attention_output = torch.cat(attention_output_chunks, dim=1) attention_output = self.output(attention_output, hidden_states) outputs = (attention_output,) if not self.local: outputs = outputs + self_outputs[1:] # add attentions if we output them else: outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them return outputs class CanineIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class CanineOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class CanineLayer(nn.Module): def __init__( self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = CanineAttention( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) self.intermediate = CanineIntermediate(config) self.output = CanineOutput(config) def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class CanineEncoder(nn.Module): def __init__( self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128, ): super().__init__() self.config = config self.layer = nn.ModuleList( [ CanineLayer( config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride, ) for _ in range(config.num_hidden_layers) ] ) self.gradient_checkpointing = False def forward( self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class CaninePooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output class CaninePredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class CanineLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = CaninePredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor: hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states class CanineOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = CanineLMPredictionHead(config) def forward( self, sequence_output: Tuple[torch.Tensor], ) -> Tuple[torch.Tensor]: prediction_scores = self.predictions(sequence_output) return prediction_scores class CaninePreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = CanineConfig load_tf_weights = load_tf_weights_in_canine base_model_prefix = "canine" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv1d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) CANINE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`CanineConfig`]): 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. """ CANINE_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *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) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) 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.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING, ) class CanineModel(CaninePreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config shallow_config = copy.deepcopy(config) shallow_config.num_hidden_layers = 1 self.char_embeddings = CanineEmbeddings(config) # shallow/low-dim transformer encoder to get a initial character encoding self.initial_char_encoder = CanineEncoder( shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride, attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride, ) self.chars_to_molecules = CharactersToMolecules(config) # deep transformer encoder self.encoder = CanineEncoder(config) self.projection = ConvProjection(config) # shallow/low-dim transformer encoder to get a final character encoding self.final_char_encoder = CanineEncoder(shallow_config) self.pooler = CaninePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask): """ Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. """ batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1] to_seq_length = to_mask.shape[1] to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float() # We don't assume that `from_tensor` is a mask (although it could be). We # don't actually care if we attend *from* padding tokens (only *to* padding) # tokens so we create a tensor of all ones. broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device) # Here we broadcast along two dimensions to create the mask. mask = broadcast_ones * to_mask return mask def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int): """Downsample 2D character attention mask to 2D molecule attention mask using MaxPool1d layer.""" # first, make char_attention_mask 3D by adding a channel dim batch_size, char_seq_len = char_attention_mask.shape poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len)) # next, apply MaxPool1d to get pooled_molecule_mask of shape (batch_size, 1, mol_seq_len) pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)( poolable_char_mask.float() ) # finally, squeeze to get tensor of shape (batch_size, mol_seq_len) molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1) return molecule_attention_mask def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor: """Repeats molecules to make them the same length as the char sequence.""" rate = self.config.downsampling_rate molecules_without_extra_cls = molecules[:, 1:, :] # `repeated`: [batch_size, almost_char_seq_len, molecule_hidden_size] repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2) # So far, we've repeated the elements sufficient for any `char_seq_length` # that's a multiple of `downsampling_rate`. Now we account for the last # n elements (n < `downsampling_rate`), i.e. the remainder of floor # division. We do this by repeating the last molecule a few extra times. last_molecule = molecules[:, -1:, :] remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item() remainder_repeated = torch.repeat_interleave( last_molecule, # +1 molecule to compensate for truncation. repeats=remainder_length + rate, dim=-2, ) # `repeated`: [batch_size, char_seq_len, molecule_hidden_size] return torch.cat([repeated, remainder_repeated], dim=-2) @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CanineModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CanineModelOutputWithPooling]: 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 ) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) molecule_attention_mask = self._downsample_attention_mask( attention_mask, downsampling_rate=self.config.downsampling_rate ) extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask( molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]) ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # `input_char_embeddings`: shape (batch_size, char_seq, char_dim) input_char_embeddings = self.char_embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, ) # Contextualize character embeddings using shallow Transformer. # We use a 3D attention mask for the local attention. # `input_char_encoding`: shape (batch_size, char_seq_len, char_dim) char_attention_mask = self._create_3d_attention_mask_from_input_mask( input_ids if input_ids is not None else inputs_embeds, attention_mask ) init_chars_encoder_outputs = self.initial_char_encoder( input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) input_char_encoding = init_chars_encoder_outputs.last_hidden_state # Downsample chars to molecules. # The following lines have dimensions: [batch, molecule_seq, molecule_dim]. # In this transformation, we change the dimensionality from `char_dim` to # `molecule_dim`, but do *NOT* add a resnet connection. Instead, we rely on # the resnet connections (a) from the final char transformer stack back into # the original char transformer stack and (b) the resnet connections from # the final char transformer stack back into the deep BERT stack of # molecules. # # Empirically, it is critical to use a powerful enough transformation here: # mean pooling causes training to diverge with huge gradient norms in this # region of the model; using a convolution here resolves this issue. From # this, it seems that molecules and characters require a very different # feature space; intuitively, this makes sense. init_molecule_encoding = self.chars_to_molecules(input_char_encoding) # Deep BERT encoder # `molecule_sequence_output`: shape (batch_size, mol_seq_len, mol_dim) encoder_outputs = self.encoder( init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) molecule_sequence_output = encoder_outputs[0] pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None # Upsample molecules back to characters. # `repeated_molecules`: shape (batch_size, char_seq_len, mol_hidden_size) repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1]) # Concatenate representations (contextualized char embeddings and repeated molecules): # `concat`: shape [batch_size, char_seq_len, molecule_hidden_size+char_hidden_final] concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1) # Project representation dimension back to hidden_size # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) sequence_output = self.projection(concat) # Apply final shallow Transformer # `sequence_output`: shape (batch_size, char_seq_len, hidden_size]) final_chars_encoder_outputs = self.final_char_encoder( sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) sequence_output = final_chars_encoder_outputs.last_hidden_state if output_hidden_states: deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1] all_hidden_states = ( all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states ) if output_attentions: deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1] all_self_attentions = ( all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions ) if not return_dict: output = (sequence_output, pooled_output) output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None) return output return CanineModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, CANINE_START_DOCSTRING, ) class CanineForSequenceClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) 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(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: 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 outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CANINE_START_DOCSTRING, ) class CanineForMultipleChoice(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model 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. """, CANINE_START_DOCSTRING, ) class CanineForTokenClassification(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) 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(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, CanineForTokenClassification >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/canine-s") >>> model = CanineForTokenClassification.from_pretrained("google/canine-s") >>> inputs = tokenizer( ... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt" ... ) >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_token_class_ids = logits.argmax(-1) >>> # Note that tokens are classified rather then input words which means that >>> # there might be more predicted token classes than words. >>> # Multiple token classes might account for the same word >>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]] >>> predicted_tokens_classes # doctest: +SKIP ``` ```python >>> labels = predicted_token_class_ids >>> loss = model(**inputs, labels=labels).loss >>> round(loss.item(), 2) # doctest: +SKIP ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CANINE_START_DOCSTRING, ) class CanineForQuestionAnswering(CaninePreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.canine = CanineModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint="Splend1dchan/canine-c-squad", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, expected_output="'nice puppet'", expected_loss=8.81, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.canine( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/canine/modeling_canine.py/0

{ "file_path": "transformers/src/transformers/models/canine/modeling_canine.py", "repo_id": "transformers", "token_count": 31277 }

289

# coding=utf-8 # Copyright 2021 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. """ CLIP model configuration""" import os from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union if TYPE_CHECKING: from ...processing_utils import ProcessorMixin from ...utils import TensorType from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/config.json", # See all CLIP models at https://huggingface.co/models?filter=clip } class CLIPTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP 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 CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 49408): Vocabulary size of the CLIP text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CLIPModel`]. hidden_size (`int`, *optional*, defaults to 512): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 2048): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. max_position_embeddings (`int`, *optional*, defaults to 77): 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 `"quick_gelu"`): 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-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). pad_token_id (`int`, *optional*, defaults to 1): Padding token id. bos_token_id (`int`, *optional*, defaults to 49406): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 49407): End of stream token id. Example: ```python >>> from transformers import CLIPTextConfig, CLIPTextModel >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPTextConfig() >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clip_text_model" def __init__( self, vocab_size=49408, hidden_size=512, intermediate_size=2048, projection_dim=512, num_hidden_layers=12, num_attention_heads=8, max_position_embeddings=77, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, # This differs from `CLIPTokenizer`'s default and from openai/clip # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 pad_token_id=1, bos_token_id=49406, eos_token_id=49407, **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.projection_dim = projection_dim 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.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout @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 CLIPConfig if config_dict.get("model_type") == "clip": 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 CLIPVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a CLIP 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 CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. 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): The number of input channels. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 32): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`): 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-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). Example: ```python >>> from transformers import CLIPVisionConfig, CLIPVisionModel >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPVisionConfig() >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, projection_dim=512, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=32, hidden_act="quick_gelu", layer_norm_eps=1e-5, attention_dropout=0.0, initializer_range=0.02, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim 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.initializer_range = initializer_range self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act @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 CLIPConfig if config_dict.get("model_type") == "clip": 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 CLIPConfig(PretrainedConfig): r""" [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate a CLIP 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 CLIP [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) 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 [`CLIPTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`CLIPVisionConfig`]. projection_dim (`int`, *optional*, defaults to 512): Dimentionality of text and vision projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* paramter. Default is used as per the original CLIP implementation. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import CLIPConfig, CLIPModel >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration >>> configuration = CLIPConfig() >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration >>> model = CLIPModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig >>> from transformers import CLIPTextConfig, CLIPVisionConfig >>> # Initializing a CLIPText and CLIPVision configuration >>> config_text = CLIPTextConfig() >>> config_vision = CLIPVisionConfig() >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision) ```""" model_type = "clip" def __init__( self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) vision_config_dict = kwargs.pop("vision_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = CLIPTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The " f'value `text_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if vision_config_dict is not None: if vision_config is None: vision_config = {} # This is the complete result when using `vision_config_dict`. _vision_config_dict = CLIPVisionConfig(**vision_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _vision_config_dict: _vision_config_dict["id2label"] = { str(key): value for key, value in _vision_config_dict["id2label"].items() } # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different. for key, value in _vision_config_dict.items(): if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]: # If specified in `vision_config_dict` if key in vision_config_dict: message = ( f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different " f'values. The value `vision_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. " f'The value `vision_config["{key}"]` will be overriden.' ) logger.info(message) # Update all values in `vision_config` with the ones in `_vision_config_dict`. vision_config.update(_vision_config_dict) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.") self.text_config = CLIPTextConfig(**text_config) self.vision_config = CLIPVisionConfig(**vision_config) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0 @classmethod def from_text_vision_configs(cls, text_config: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs): r""" Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model configuration. Returns: [`CLIPConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) class CLIPOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ("attention_mask", {0: "batch", 1: "sequence"}), ] ) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("logits_per_image", {0: "batch"}), ("logits_per_text", {0: "batch"}), ("text_embeds", {0: "batch"}), ("image_embeds", {0: "batch"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4 def generate_dummy_inputs( self, processor: "ProcessorMixin", batch_size: int = -1, seq_length: int = -1, framework: Optional["TensorType"] = None, ) -> Mapping[str, Any]: text_input_dict = super().generate_dummy_inputs( processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework ) image_input_dict = super().generate_dummy_inputs( processor.image_processor, batch_size=batch_size, framework=framework ) return {**text_input_dict, **image_input_dict} @property def default_onnx_opset(self) -> int: return 14

transformers/src/transformers/models/clip/configuration_clip.py/0

{ "file_path": "transformers/src/transformers/models/clip/configuration_clip.py", "repo_id": "transformers", "token_count": 8388 }

290

# coding=utf-8 # Copyright 2023 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. """ CLVP model configuration""" import os from typing import TYPE_CHECKING, Union if TYPE_CHECKING: pass from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) CLVP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "susnato/clvp_dev": "https://huggingface.co/susnato/clvp_dev/resolve/main/config.json", } class ClvpEncoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults will yield a similar configuration to that of the encoder of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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 256): Vocabulary size of the CLVP Encoder model. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. projection_dim (`int`, *optional*, defaults to 768): Dimensionality of the projection vector. num_hidden_layers (`int`, *optional*, defaults to 20): 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. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): 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-05): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the feed-forward layers in [`ClvpEncoderMLP`]. use_rotary_embedding (`bool`, *optional*, defaults to `True`): Whether to use rotary_embedding or not. use_attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in Query, Key and Value layers during self attention. summary_type (`str`, *optional*, defaults to `"mean"`): What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and `"cls_index"` are supported. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). bos_token_id (`int`, *optional*, defaults to 255): Beginning of sequence token id. eos_token_id (`int`, *optional*, defaults to 0): End of sequence token id. Example: ```python >>> from transformers import ClvpEncoderConfig, ClvpEncoder >>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration >>> encoder_configuration = ClvpEncoderConfig() >>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpEncoder(encoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clvp_encoder" def __init__( self, vocab_size=256, hidden_size=768, intermediate_size=1536, projection_dim=768, num_hidden_layers=20, num_attention_heads=12, hidden_act="gelu", layer_norm_eps=1e-5, attention_dropout=0.1, dropout=0.1, use_rotary_embedding=True, use_attention_bias=False, summary_type="mean", initializer_factor=1.0, bos_token_id=255, eos_token_id=0, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_factor = initializer_factor self.attention_dropout = attention_dropout self.dropout = dropout self.use_rotary_embedding = use_rotary_embedding self.use_attention_bias = use_attention_bias self.summary_type = summary_type self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @classmethod def from_pretrained( cls, pretrained_model_name_or_path: Union[str, os.PathLike], config_type: str = "text_config", **kwargs ) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # make sure to have the config_type be either "text_config" or "speech_config" # this is to make sure that we can load only text or speech configs from the nested ClvpConfig. if config_type not in ["text_config", "speech_config"]: raise ValueError( f"We can only load either 'text_config' or 'speech_config' but you are trying to load" f"{config_type}" ) # get the text config dict if we are loading from ClvpConfig if config_dict.get("model_type") == "clvp": config_dict = config_dict[config_type] 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 ClvpDecoderConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP Decoder Model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Decoder part of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. The architecture is similar to GPT2. Args: vocab_size (`int`, *optional*, defaults to 8194): Vocabulary size of the model. max_position_embeddings (`int`, *optional*, defaults to 608): The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. max_text_tokens (`int`, *optional*, defaults to 404): The maximum sequence length of text tokens that this model might ever be used with. Similar to `n_positions` in `GPT2Config`. hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the embeddings and hidden states. num_hidden_layers (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`. num_mel_attn_blocks (`int`, *optional*, defaults to 6): Denotes the number of self attention layers in [`ClvpConditioningEncoder`]. activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio to be used after the projection and activation. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). bos_token_id (`int`, *optional*, defaults to 8192): Beginning of sequence token id, used at the start of the generation. eos_token_id (`int`, *optional*, defaults to 8193): End of sequence token id, used in the method [`ClvpModelForConditionalGeneration.fix_speech_decoder_output()`] to correct decoder outputs. feature_size (`int`, *optional*, defaults to 80): The feature dimension of the extracted mel features. This value is used in [`ClvpConditioningEncoder`]. use_attention_bias (`bool`, *optional*, defaults to `True`): Whether to use bias in Query, Key and Value layers during self attention. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`): These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs. Example: ```python >>> from transformers import ClvpDecoderConfig, ClvpDecoder >>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration >>> decoder_configuration = ClvpDecoderConfig() >>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpDecoder(decoder_configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "clvp_decoder" def __init__( self, vocab_size=8194, max_position_embeddings=608, max_text_tokens=404, hidden_size=1024, num_hidden_layers=30, num_attention_heads=16, n_inner=None, num_mel_attn_blocks=6, activation_function="gelu_new", resid_pdrop=0.1, embd_pdrop=0.1, attention_dropout=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, use_cache=True, bos_token_id=8192, eos_token_id=8193, feature_size=80, use_attention_bias=True, initializer_factor=1.0, decoder_fixing_codes=[83, 45, 45, 248], **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.max_text_tokens = max_text_tokens self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_inner = n_inner self.num_mel_attn_blocks = num_mel_attn_blocks self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attention_dropout = attention_dropout self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.use_cache = use_cache self.feature_size = feature_size self.use_attention_bias = use_attention_bias self.initializer_factor = initializer_factor self.decoder_fixing_codes = decoder_fixing_codes self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @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 speech config dict if we are loading from ClvpConfig if config_dict.get("model_type") == "clvp": config_dict = config_dict["decoder_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 ClvpConfig(PretrainedConfig): r""" [`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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 the CLVP text encoder. speech_config (`dict`, *optional*): Dictionary of configuration options used to initialize CLVP speech encoder. decoder_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`ClvpDecoderConfig`]. projection_dim (`int`, *optional*, defaults to 768): Dimentionality of text and speech projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The inital value of the *logit_scale* paramter. Default is used as per the original CLVP implementation. initializer_factor (`float`, *optional*, defaults to 1.0): A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization testing). kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration >>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration >>> configuration = ClvpConfig() >>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration >>> model = ClvpModelForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig >>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig >>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration >>> config_text = ClvpEncoderConfig() >>> config_speech = ClvpEncoderConfig() >>> decoder_config = ClvpDecoderConfig() >>> config = ClvpConfig.from_sub_model_configs(config_text, config_speech, decoder_config) ```""" model_type = "clvp" is_composition = True def __init__( self, text_config=None, speech_config=None, decoder_config=None, projection_dim=768, logit_scale_init_value=2.6592, initializer_factor=1.0, **kwargs, ): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values.") if speech_config is None: speech_config = {} logger.info("`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.") if decoder_config is None: decoder_config = {} logger.info("`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values.") self.text_config = ClvpEncoderConfig(**text_config) self.speech_config = ClvpEncoderConfig(**speech_config) self.decoder_config = ClvpDecoderConfig(**decoder_config) self.projection_dim = projection_dim self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = initializer_factor @classmethod def from_sub_model_configs( cls, text_config: ClvpEncoderConfig, speech_config: ClvpEncoderConfig, decoder_config: ClvpDecoderConfig, **kwargs, ): r""" Instantiate a [`ClvpConfig`] (or a derived class) from CLVP text model configuration, CLVP speech model configuration and CLVP decoder model configuration. Args: text_config (`ClvpEncoderConfig`): Text model configuration of type [`ClvpEncoderConfig`]. speech_config (`ClvpEncoderConfig`): Speech model configuration of type [`ClvpEncoderConfig`]. decoder_config (`ClvpDecoderConfig`): Decoder model configuration of type [`ClvpDecoderConfig`]. Returns: [`ClvpConfig`]: An instance of a configuration object """ return cls( text_config=text_config.to_dict(), speech_config=speech_config.to_dict(), decoder_config=decoder_config.to_dict(), **kwargs, )

transformers/src/transformers/models/clvp/configuration_clvp.py/0

{ "file_path": "transformers/src/transformers/models/clvp/configuration_clvp.py", "repo_id": "transformers", "token_count": 8205 }

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# coding=utf-8 # Copyright 2024 Cohere team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """ Cohere model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) COHERE_PRETRAINED_CONFIG_ARCHIVE_MAP = {} class CohereConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere 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. Instantiating a configuration with the defaults will yield a similar configuration to that of the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) model. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Cohere model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`CohereModel`] hidden_size (`int`, *optional*, defaults to 8192): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22528): Dimension of the MLP representations. logit_scale (`float`, *optional*, defaults to 0.0625): The scaling factor for the output logits. num_hidden_layers (`int`, *optional*, defaults to 40): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 64): 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`. 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 8192): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization. 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`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 5): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 255001): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import CohereModel, CohereConfig >>> # Initializing a Cohere model configuration >>> configuration = CohereConfig() >>> # Initializing a model from the Cohere configuration >>> model = CohereModel(configuration) # doctest: +SKIP >>> # Accessing the model configuration >>> configuration = model.config # doctest: +SKIP ```""" model_type = "cohere" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=256000, hidden_size=8192, intermediate_size=22528, logit_scale=0.0625, num_hidden_layers=40, num_attention_heads=64, num_key_value_heads=None, hidden_act="silu", max_position_embeddings=8192, initializer_range=0.02, layer_norm_eps=1e-5, use_cache=True, pad_token_id=0, bos_token_id=5, eos_token_id=255001, tie_word_embeddings=True, rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.logit_scale = logit_scale self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_bias = attention_bias self.attention_dropout = attention_dropout super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, )

transformers/src/transformers/models/cohere/configuration_cohere.py/0

{ "file_path": "transformers/src/transformers/models/cohere/configuration_cohere.py", "repo_id": "transformers", "token_count": 2700 }

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# Copyright 2022 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. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) _import_structure = { "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_convnext"] = ["ConvNextFeatureExtractor"] _import_structure["image_processing_convnext"] = ["ConvNextImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_convnext"] = [ "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvNextForImageClassification", "ConvNextModel", "ConvNextPreTrainedModel", "ConvNextBackbone", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_convnext"] = [ "TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextPreTrainedModel", ] if TYPE_CHECKING: from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_convnext import ConvNextFeatureExtractor from .image_processing_convnext import ConvNextImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_convnext import ( CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvNextBackbone, ConvNextForImageClassification, ConvNextModel, ConvNextPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

transformers/src/transformers/models/convnext/__init__.py/0

{ "file_path": "transformers/src/transformers/models/convnext/__init__.py", "repo_id": "transformers", "token_count": 1189 }

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# coding=utf-8 # Copyright 2022 The OpenBMB Team 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. """ CPMAnt model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json" # See all CPMAnt models at https://huggingface.co/models?filter=cpmant } class CpmAntConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`CpmAntModel`]. It is used to instantiate an CPMAnt model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the CPMAnt [openbmb/cpm-ant-10b](https://huggingface.co/openbmb/cpm-ant-10b) 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 30720): Vocabulary size of the CPMAnt model. Defines the number of different tokens that can be represented by the `input` passed when calling [`CpmAntModel`]. hidden_size (`int`, *optional*, defaults to 4096): Dimension of the encoder layers. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads in the Transformer encoder. dim_head (`int`, *optional*, defaults to 128): Dimension of attention heads for each attention layer in the Transformer encoder. dim_ff (`int`, *optional*, defaults to 10240): Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 48): Number of layers of the Transformer encoder. dropout_p (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder. position_bias_num_buckets (`int`, *optional*, defaults to 512): The number of position_bias buckets. position_bias_max_distance (`int`, *optional*, defaults to 2048): 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). eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. init_std (`float`, *optional*, defaults to 1.0): Initialize parameters with std = init_std. prompt_types (`int`, *optional*, defaults to 32): The type of prompt. prompt_length (`int`, *optional*, defaults to 32): The length of prompt. segment_types (`int`, *optional*, defaults to 32): The type of segment. use_cache (`bool`, *optional*, defaults to `True`): Whether to use cache. Example: ```python >>> from transformers import CpmAntModel, CpmAntConfig >>> # Initializing a CPMAnt cpm-ant-10b style configuration >>> configuration = CpmAntConfig() >>> # Initializing a model from the cpm-ant-10b style configuration >>> model = CpmAntModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "cpmant" def __init__( self, vocab_size: int = 30720, hidden_size: int = 4096, num_attention_heads: int = 32, dim_head: int = 128, dim_ff: int = 10240, num_hidden_layers: int = 48, dropout_p: int = 0.0, position_bias_num_buckets: int = 512, position_bias_max_distance: int = 2048, eps: int = 1e-6, init_std: float = 1.0, prompt_types: int = 32, prompt_length: int = 32, segment_types: int = 32, use_cache: bool = True, **kwargs, ): super().__init__(**kwargs) self.prompt_types = prompt_types self.prompt_length = prompt_length self.segment_types = segment_types self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.dim_head = dim_head self.dim_ff = dim_ff self.num_hidden_layers = num_hidden_layers self.position_bias_num_buckets = position_bias_num_buckets self.position_bias_max_distance = position_bias_max_distance self.dropout_p = dropout_p self.eps = eps self.use_cache = use_cache self.vocab_size = vocab_size self.init_std = init_std

transformers/src/transformers/models/cpmant/configuration_cpmant.py/0

{ "file_path": "transformers/src/transformers/models/cpmant/configuration_cpmant.py", "repo_id": "transformers", "token_count": 2021 }

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# coding=utf-8 # Copyright Meta Platforms 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. """ Data2VecVision model configuration""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/data2vec-vision-base-ft": ( "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json" ), } class Data2VecVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Data2VecVisionModel`]. It is used to instantiate an Data2VecVision model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Data2VecVision [facebook/data2vec-vision-base](https://huggingface.co/facebook/data2vec-vision-base) architecture. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. 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. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. 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. num_channels (`int`, *optional*, defaults to 3): The number of input channels. use_mask_token (`bool`, *optional*, defaults to `False`): Whether to use a mask token for masked image modeling. use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`): Whether to use BERT-style absolute position embeddings. use_relative_position_bias (`bool`, *optional*, defaults to `False`): Whether to use T5-style relative position embeddings in the self-attention layers. use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`): Whether to use the same relative position embeddings across all self-attention layers of the Transformer. layer_scale_init_value (`float`, *optional*, defaults to 0.1): Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate per sample (when applied in the main path of residual layers). use_mean_pooling (`bool`, *optional*, defaults to `True`): Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the CLS token, before applying the classification head. out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`): Indices of the feature maps to use for semantic segmentation. pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): Pooling scales used in Pooling Pyramid Module applied on the last feature map. use_auxiliary_head (`bool`, *optional*, defaults to `True`): Whether to use an auxiliary head during training. auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): Weight of the cross-entropy loss of the auxiliary head. auxiliary_channels (`int`, *optional*, defaults to 256): Number of channels to use in the auxiliary head. auxiliary_num_convs (`int`, *optional*, defaults to 1): Number of convolutional layers to use in the auxiliary head. auxiliary_concat_input (`bool`, *optional*, defaults to `False`): Whether to concatenate the output of the auxiliary head with the input before the classification layer. semantic_loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. Example: ```python >>> from transformers import Data2VecVisionConfig, Data2VecVisionModel >>> # Initializing a Data2VecVision data2vec_vision-base-patch16-224-in22k style configuration >>> configuration = Data2VecVisionConfig() >>> # Initializing a model (with random weights) from the data2vec_vision-base-patch16-224-in22k style configuration >>> model = Data2VecVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "data2vec-vision" def __init__( self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, image_size=224, patch_size=16, num_channels=3, use_mask_token=False, use_absolute_position_embeddings=False, use_relative_position_bias=False, use_shared_relative_position_bias=False, layer_scale_init_value=0.1, drop_path_rate=0.1, use_mean_pooling=True, out_indices=[3, 5, 7, 11], pool_scales=[1, 2, 3, 6], use_auxiliary_head=True, auxiliary_loss_weight=0.4, auxiliary_channels=256, auxiliary_num_convs=1, auxiliary_concat_input=False, semantic_loss_ignore_index=255, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.use_mask_token = use_mask_token self.use_absolute_position_embeddings = use_absolute_position_embeddings self.use_relative_position_bias = use_relative_position_bias self.use_shared_relative_position_bias = use_shared_relative_position_bias self.layer_scale_init_value = layer_scale_init_value self.drop_path_rate = drop_path_rate self.use_mean_pooling = use_mean_pooling # decode head attributes (semantic segmentation) self.out_indices = out_indices self.pool_scales = pool_scales # auxiliary head attributes (semantic segmentation) self.use_auxiliary_head = use_auxiliary_head self.auxiliary_loss_weight = auxiliary_loss_weight self.auxiliary_channels = auxiliary_channels self.auxiliary_num_convs = auxiliary_num_convs self.auxiliary_concat_input = auxiliary_concat_input self.semantic_loss_ignore_index = semantic_loss_ignore_index # Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig class Data2VecVisionOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4

transformers/src/transformers/models/data2vec/configuration_data2vec_vision.py/0

{ "file_path": "transformers/src/transformers/models/data2vec/configuration_data2vec_vision.py", "repo_id": "transformers", "token_count": 3576 }

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# coding=utf-8 # Copyright 2020 Microsoft and the Hugging Face Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch DeBERTa-v2 model.""" from collections.abc import Sequence from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutput, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import softmax_backward_data from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_deberta_v2 import DebertaV2Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "DebertaV2Config" _CHECKPOINT_FOR_DOC = "microsoft/deberta-v2-xlarge" _QA_TARGET_START_INDEX = 2 _QA_TARGET_END_INDEX = 9 DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/deberta-v2-xlarge", "microsoft/deberta-v2-xxlarge", "microsoft/deberta-v2-xlarge-mnli", "microsoft/deberta-v2-xxlarge-mnli", ] # Copied from transformers.models.deberta.modeling_deberta.ContextPooler class ContextPooler(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size) self.dropout = StableDropout(config.pooler_dropout) self.config = config def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. context_token = hidden_states[:, 0] context_token = self.dropout(context_token) pooled_output = self.dense(context_token) pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output) return pooled_output @property def output_dim(self): return self.config.hidden_size # Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2 class XSoftmax(torch.autograd.Function): """ Masked Softmax which is optimized for saving memory Args: input (`torch.tensor`): The input tensor that will apply softmax. mask (`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation. dim (int): The dimension that will apply softmax Example: ```python >>> import torch >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax >>> # Make a tensor >>> x = torch.randn([4, 20, 100]) >>> # Create a mask >>> mask = (x > 0).int() >>> # Specify the dimension to apply softmax >>> dim = -1 >>> y = XSoftmax.apply(x, mask, dim) ```""" @staticmethod def forward(self, input, mask, dim): self.dim = dim rmask = ~(mask.to(torch.bool)) output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min)) output = torch.softmax(output, self.dim) output.masked_fill_(rmask, 0) self.save_for_backward(output) return output @staticmethod def backward(self, grad_output): (output,) = self.saved_tensors inputGrad = softmax_backward_data(self, grad_output, output, self.dim, output) return inputGrad, None, None @staticmethod def symbolic(g, self, mask, dim): import torch.onnx.symbolic_helper as sym_help from torch.onnx.symbolic_opset9 import masked_fill, softmax mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"]) r_mask = g.op( "Cast", g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value), to_i=sym_help.cast_pytorch_to_onnx["Bool"], ) output = masked_fill( g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min)) ) output = softmax(g, output, dim) return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool))) # Copied from transformers.models.deberta.modeling_deberta.DropoutContext class DropoutContext(object): def __init__(self): self.dropout = 0 self.mask = None self.scale = 1 self.reuse_mask = True # Copied from transformers.models.deberta.modeling_deberta.get_mask def get_mask(input, local_context): if not isinstance(local_context, DropoutContext): dropout = local_context mask = None else: dropout = local_context.dropout dropout *= local_context.scale mask = local_context.mask if local_context.reuse_mask else None if dropout > 0 and mask is None: mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool) if isinstance(local_context, DropoutContext): if local_context.mask is None: local_context.mask = mask return mask, dropout # Copied from transformers.models.deberta.modeling_deberta.XDropout class XDropout(torch.autograd.Function): """Optimized dropout function to save computation and memory by using mask operation instead of multiplication.""" @staticmethod def forward(ctx, input, local_ctx): mask, dropout = get_mask(input, local_ctx) ctx.scale = 1.0 / (1 - dropout) if dropout > 0: ctx.save_for_backward(mask) return input.masked_fill(mask, 0) * ctx.scale else: return input @staticmethod def backward(ctx, grad_output): if ctx.scale > 1: (mask,) = ctx.saved_tensors return grad_output.masked_fill(mask, 0) * ctx.scale, None else: return grad_output, None @staticmethod def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: Union[float, DropoutContext]) -> torch._C.Value: from torch.onnx import symbolic_opset12 dropout_p = local_ctx if isinstance(local_ctx, DropoutContext): dropout_p = local_ctx.dropout # StableDropout only calls this function when training. train = True # TODO: We should check if the opset_version being used to export # is > 12 here, but there's no good way to do that. As-is, if the # opset_version < 12, export will fail with a CheckerError. # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like: # if opset_version < 12: # return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train) return symbolic_opset12.dropout(g, input, dropout_p, train) # Copied from transformers.models.deberta.modeling_deberta.StableDropout class StableDropout(nn.Module): """ Optimized dropout module for stabilizing the training Args: drop_prob (float): the dropout probabilities """ def __init__(self, drop_prob): super().__init__() self.drop_prob = drop_prob self.count = 0 self.context_stack = None def forward(self, x): """ Call the module Args: x (`torch.tensor`): The input tensor to apply dropout """ if self.training and self.drop_prob > 0: return XDropout.apply(x, self.get_context()) return x def clear_context(self): self.count = 0 self.context_stack = None def init_context(self, reuse_mask=True, scale=1): if self.context_stack is None: self.context_stack = [] self.count = 0 for c in self.context_stack: c.reuse_mask = reuse_mask c.scale = scale def get_context(self): if self.context_stack is not None: if self.count >= len(self.context_stack): self.context_stack.append(DropoutContext()) ctx = self.context_stack[self.count] ctx.dropout = self.drop_prob self.count += 1 return ctx else: return self.drop_prob # Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm class DebertaV2SelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2 class DebertaV2Attention(nn.Module): def __init__(self, config): super().__init__() self.self = DisentangledSelfAttention(config) self.output = DebertaV2SelfOutput(config) self.config = config def forward( self, hidden_states, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ): self_output = self.self( hidden_states, attention_mask, output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if output_attentions: self_output, att_matrix = self_output if query_states is None: query_states = hidden_states attention_output = self.output(self_output, query_states) if output_attentions: return (attention_output, att_matrix) else: return attention_output # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2 class DebertaV2Intermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm class DebertaV2Output(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.config = config def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states # Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2 class DebertaV2Layer(nn.Module): def __init__(self, config): super().__init__() self.attention = DebertaV2Attention(config) self.intermediate = DebertaV2Intermediate(config) self.output = DebertaV2Output(config) def forward( self, hidden_states, attention_mask, query_states=None, relative_pos=None, rel_embeddings=None, output_attentions=False, ): attention_output = self.attention( hidden_states, attention_mask, output_attentions=output_attentions, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, ) if output_attentions: attention_output, att_matrix = attention_output intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) if output_attentions: return (layer_output, att_matrix) else: return layer_output class ConvLayer(nn.Module): def __init__(self, config): super().__init__() kernel_size = getattr(config, "conv_kernel_size", 3) groups = getattr(config, "conv_groups", 1) self.conv_act = getattr(config, "conv_act", "tanh") self.conv = nn.Conv1d( config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups ) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.config = config def forward(self, hidden_states, residual_states, input_mask): out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous() rmask = (1 - input_mask).bool() out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0) out = ACT2FN[self.conv_act](self.dropout(out)) layer_norm_input = residual_states + out output = self.LayerNorm(layer_norm_input).to(layer_norm_input) if input_mask is None: output_states = output else: if input_mask.dim() != layer_norm_input.dim(): if input_mask.dim() == 4: input_mask = input_mask.squeeze(1).squeeze(1) input_mask = input_mask.unsqueeze(2) input_mask = input_mask.to(output.dtype) output_states = output * input_mask return output_states class DebertaV2Encoder(nn.Module): """Modified BertEncoder with relative position bias support""" def __init__(self, config): super().__init__() self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)]) self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.position_buckets = getattr(config, "position_buckets", -1) pos_ebd_size = self.max_relative_positions * 2 if self.position_buckets > 0: pos_ebd_size = self.position_buckets * 2 self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size) self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")] if "layer_norm" in self.norm_rel_ebd: self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None self.gradient_checkpointing = False def get_rel_embedding(self): rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd): rel_embeddings = self.LayerNorm(rel_embeddings) return rel_embeddings def get_attention_mask(self, attention_mask): if attention_mask.dim() <= 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1) elif attention_mask.dim() == 3: attention_mask = attention_mask.unsqueeze(1) return attention_mask def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): if self.relative_attention and relative_pos is None: q = query_states.size(-2) if query_states is not None else hidden_states.size(-2) relative_pos = build_relative_position( q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions, device=hidden_states.device, ) return relative_pos def forward( self, hidden_states, attention_mask, output_hidden_states=True, output_attentions=False, query_states=None, relative_pos=None, return_dict=True, ): if attention_mask.dim() <= 2: input_mask = attention_mask else: input_mask = attention_mask.sum(-2) > 0 attention_mask = self.get_attention_mask(attention_mask) relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None if isinstance(hidden_states, Sequence): next_kv = hidden_states[0] else: next_kv = hidden_states rel_embeddings = self.get_rel_embedding() output_states = next_kv for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (output_states,) if self.gradient_checkpointing and self.training: output_states = self._gradient_checkpointing_func( layer_module.__call__, next_kv, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions, ) else: output_states = layer_module( next_kv, attention_mask, query_states=query_states, relative_pos=relative_pos, rel_embeddings=rel_embeddings, output_attentions=output_attentions, ) if output_attentions: output_states, att_m = output_states if i == 0 and self.conv is not None: output_states = self.conv(hidden_states, output_states, input_mask) if query_states is not None: query_states = output_states if isinstance(hidden_states, Sequence): next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None else: next_kv = output_states if output_attentions: all_attentions = all_attentions + (att_m,) if output_hidden_states: all_hidden_states = all_hidden_states + (output_states,) if not return_dict: return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions ) def make_log_bucket_position(relative_pos, bucket_size, max_position): sign = torch.sign(relative_pos) mid = bucket_size // 2 abs_pos = torch.where( (relative_pos < mid) & (relative_pos > -mid), torch.tensor(mid - 1).type_as(relative_pos), torch.abs(relative_pos), ) log_pos = ( torch.ceil(torch.log(abs_pos / mid) / torch.log(torch.tensor((max_position - 1) / mid)) * (mid - 1)) + mid ) bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos), log_pos * sign) return bucket_pos def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1, device=None): """ Build relative position according to the query and key We assume the absolute position of query \$P_q\$ is range from (0, query_size) and the absolute position of key \$P_k\$ is range from (0, key_size), The relative positions from query to key is \$R_{q \\rightarrow k} = P_q - P_k\$ Args: query_size (int): the length of query key_size (int): the length of key bucket_size (int): the size of position bucket max_position (int): the maximum allowed absolute position device (`torch.device`): the device on which tensors will be created. Return: `torch.LongTensor`: A tensor with shape [1, query_size, key_size] """ q_ids = torch.arange(0, query_size, device=device) k_ids = torch.arange(0, key_size, device=device) rel_pos_ids = q_ids[:, None] - k_ids[None, :] if bucket_size > 0 and max_position > 0: rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position) rel_pos_ids = rel_pos_ids.to(torch.long) rel_pos_ids = rel_pos_ids[:query_size, :] rel_pos_ids = rel_pos_ids.unsqueeze(0) return rel_pos_ids @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]) @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)]) @torch.jit.script # Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand def pos_dynamic_expand(pos_index, p2c_att, key_layer): return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))) class DisentangledSelfAttention(nn.Module): """ Disentangled self-attention module Parameters: config (`DebertaV2Config`): A model config class instance with the configuration to build a new model. The schema is similar to *BertConfig*, for more details, please refer [`DebertaV2Config`] """ def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads _attention_head_size = config.hidden_size // config.num_attention_heads self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) self.share_att_key = getattr(config, "share_att_key", False) self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] self.relative_attention = getattr(config, "relative_attention", False) if self.relative_attention: self.position_buckets = getattr(config, "position_buckets", -1) self.max_relative_positions = getattr(config, "max_relative_positions", -1) if self.max_relative_positions < 1: self.max_relative_positions = config.max_position_embeddings self.pos_ebd_size = self.max_relative_positions if self.position_buckets > 0: self.pos_ebd_size = self.position_buckets self.pos_dropout = StableDropout(config.hidden_dropout_prob) if not self.share_att_key: if "c2p" in self.pos_att_type: self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) if "p2c" in self.pos_att_type: self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = StableDropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, attention_heads): new_x_shape = x.size()[:-1] + (attention_heads, -1) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1)) def forward( self, hidden_states, attention_mask, output_attentions=False, query_states=None, relative_pos=None, rel_embeddings=None, ): """ Call the module Args: hidden_states (`torch.FloatTensor`): Input states to the module usually the output from previous layer, it will be the Q,K and V in *Attention(Q,K,V)* attention_mask (`torch.BoolTensor`): An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j* th token. output_attentions (`bool`, optional): Whether return the attention matrix. query_states (`torch.FloatTensor`, optional): The *Q* state in *Attention(Q,K,V)*. relative_pos (`torch.LongTensor`): The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with values ranging in [*-max_relative_positions*, *max_relative_positions*]. rel_embeddings (`torch.FloatTensor`): The embedding of relative distances. It's a tensor of shape [\$2 \\times \\text{max_relative_positions}\$, *hidden_size*]. """ if query_states is None: query_states = hidden_states query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads) key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads) value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads) rel_att = None # Take the dot product between "query" and "key" to get the raw attention scores. scale_factor = 1 if "c2p" in self.pos_att_type: scale_factor += 1 if "p2c" in self.pos_att_type: scale_factor += 1 scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor) attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2) / scale.to(dtype=query_layer.dtype)) if self.relative_attention: rel_embeddings = self.pos_dropout(rel_embeddings) rel_att = self.disentangled_attention_bias( query_layer, key_layer, relative_pos, rel_embeddings, scale_factor ) if rel_att is not None: attention_scores = attention_scores + rel_att attention_scores = attention_scores attention_scores = attention_scores.view( -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1) ) # bsz x height x length x dimension attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) attention_probs = self.dropout(attention_probs) context_layer = torch.bmm( attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer ) context_layer = ( context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1)) .permute(0, 2, 1, 3) .contiguous() ) new_context_layer_shape = context_layer.size()[:-2] + (-1,) context_layer = context_layer.view(new_context_layer_shape) if output_attentions: return (context_layer, attention_probs) else: return context_layer def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): if relative_pos is None: q = query_layer.size(-2) relative_pos = build_relative_position( q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions, device=query_layer.device, ) if relative_pos.dim() == 2: relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) elif relative_pos.dim() == 3: relative_pos = relative_pos.unsqueeze(1) # bsz x height x query x key elif relative_pos.dim() != 4: raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}") att_span = self.pos_ebd_size relative_pos = relative_pos.long().to(query_layer.device) rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0) if self.share_att_key: pos_query_layer = self.transpose_for_scores( self.query_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat( query_layer.size(0) // self.num_attention_heads, 1, 1 ) else: if "c2p" in self.pos_att_type: pos_key_layer = self.transpose_for_scores( self.pos_key_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) if "p2c" in self.pos_att_type: pos_query_layer = self.transpose_for_scores( self.pos_query_proj(rel_embeddings), self.num_attention_heads ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) score = 0 # content->position if "c2p" in self.pos_att_type: scale = torch.sqrt(torch.tensor(pos_key_layer.size(-1), dtype=torch.float) * scale_factor) c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2)) c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1) c2p_att = torch.gather( c2p_att, dim=-1, index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]), ) score += c2p_att / scale.to(dtype=c2p_att.dtype) # position->content if "p2c" in self.pos_att_type: scale = torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor) if key_layer.size(-2) != query_layer.size(-2): r_pos = build_relative_position( key_layer.size(-2), key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions, device=query_layer.device, ) r_pos = r_pos.unsqueeze(0) else: r_pos = relative_pos p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2)) p2c_att = torch.gather( p2c_att, dim=-1, index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]), ).transpose(-1, -2) score += p2c_att / scale.to(dtype=p2c_att.dtype) return score # Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm class DebertaV2Embeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() pad_token_id = getattr(config, "pad_token_id", 0) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id) self.position_biased_input = getattr(config, "position_biased_input", True) if not self.position_biased_input: self.position_embeddings = None else: self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size) if config.type_vocab_size > 0: self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size) if self.embedding_size != config.hidden_size: self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False) self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) self.dropout = StableDropout(config.hidden_dropout_prob) self.config = config # 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=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if self.position_embeddings is not None: position_embeddings = self.position_embeddings(position_ids.long()) else: position_embeddings = torch.zeros_like(inputs_embeds) embeddings = inputs_embeds if self.position_biased_input: embeddings += position_embeddings if self.config.type_vocab_size > 0: token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings += token_type_embeddings if self.embedding_size != self.config.hidden_size: embeddings = self.embed_proj(embeddings) embeddings = self.LayerNorm(embeddings) if mask is not None: if mask.dim() != embeddings.dim(): if mask.dim() == 4: mask = mask.squeeze(1).squeeze(1) mask = mask.unsqueeze(2) mask = mask.to(embeddings.dtype) embeddings = embeddings * mask embeddings = self.dropout(embeddings) return embeddings # Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2 class DebertaV2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = DebertaV2Config base_model_prefix = "deberta" _keys_to_ignore_on_load_unexpected = ["position_embeddings"] supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() DEBERTA_START_DOCSTRING = r""" The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data. 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 ([`DebertaV2Config`]): 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. """ DEBERTA_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *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) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) 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.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.", DEBERTA_START_DOCSTRING, ) # Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2 class DebertaV2Model(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.embeddings = DebertaV2Embeddings(config) self.encoder = DebertaV2Encoder(config) self.z_steps = 0 self.config = config # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, new_embeddings): self.embeddings.word_embeddings = new_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError("The prune function is not implemented in DeBERTa model.") @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: 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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, mask=attention_mask, inputs_embeds=inputs_embeds, ) encoder_outputs = self.encoder( embedding_output, attention_mask, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict, ) encoded_layers = encoder_outputs[1] if self.z_steps > 1: hidden_states = encoded_layers[-2] layers = [self.encoder.layer[-1] for _ in range(self.z_steps)] query_states = encoded_layers[-1] rel_embeddings = self.encoder.get_rel_embedding() attention_mask = self.encoder.get_attention_mask(attention_mask) rel_pos = self.encoder.get_rel_pos(embedding_output) for layer in layers[1:]: query_states = layer( hidden_states, attention_mask, output_attentions=False, query_states=query_states, relative_pos=rel_pos, rel_embeddings=rel_embeddings, ) encoded_layers.append(query_states) sequence_output = encoded_layers[-1] if not return_dict: return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states if output_hidden_states else None, attentions=encoder_outputs.attentions, ) @add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING) class DebertaV2ForMaskedLM(DebertaV2PreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"] def __init__(self, config): super().__init__(config) self.deberta = DebertaV2Model(config) self.cls = DebertaV2OnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, mask="[MASK]", ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM.forward with Deberta->DebertaV2 def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MaskedLMOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (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]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Copied from transformers.models.deberta.modeling_deberta.DebertaPredictionHeadTransform with Deberta->DebertaV2 class DebertaV2PredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.embedding_size = getattr(config, "embedding_size", config.hidden_size) self.dense = nn.Linear(config.hidden_size, self.embedding_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(self.embedding_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states # Copied from transformers.models.deberta.modeling_deberta.DebertaLMPredictionHead with Deberta->DebertaV2 class DebertaV2LMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = DebertaV2PredictionHeadTransform(config) self.embedding_size = getattr(config, "embedding_size", config.hidden_size) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(self.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta class DebertaV2OnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = DebertaV2LMPredictionHead(config) def forward(self, sequence_output): prediction_scores = self.predictions(sequence_output) return prediction_scores @add_start_docstrings( """ DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, DEBERTA_START_DOCSTRING, ) class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) num_labels = getattr(config, "num_labels", 2) self.num_labels = num_labels self.deberta = DebertaV2Model(config) self.pooler = ContextPooler(config) output_dim = self.pooler.output_dim self.classifier = nn.Linear(output_dim, num_labels) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out self.dropout = StableDropout(drop_out) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.deberta.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.deberta.set_input_embeddings(new_embeddings) @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2 def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutput]: 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 outputs = self.deberta( input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pooler(encoder_layer) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: # regression task loss_fn = nn.MSELoss() logits = logits.view(-1).to(labels.dtype) loss = loss_fn(logits, labels.view(-1)) elif labels.dim() == 1 or labels.size(-1) == 1: label_index = (labels >= 0).nonzero() labels = labels.long() if label_index.size(0) > 0: labeled_logits = torch.gather( logits, 0, label_index.expand(label_index.size(0), logits.size(1)) ) labels = torch.gather(labels, 0, label_index.view(-1)) loss_fct = CrossEntropyLoss() loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1)) else: loss = torch.tensor(0).to(logits) else: log_softmax = nn.LogSoftmax(-1) loss = -((log_softmax(logits) * labels).sum(-1)).mean() elif self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) @add_start_docstrings( """ DeBERTa Model 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. """, DEBERTA_START_DOCSTRING, ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2 class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.deberta = DebertaV2Model(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) 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(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions ) @add_start_docstrings( """ DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, DEBERTA_START_DOCSTRING, ) class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.deberta = DebertaV2Model(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, qa_target_start_index=_QA_TARGET_START_INDEX, qa_target_end_index=_QA_TARGET_END_INDEX, ) # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2 def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.deberta( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ DeBERTa Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, DEBERTA_START_DOCSTRING, ) class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel): def __init__(self, config): super().__init__(config) num_labels = getattr(config, "num_labels", 2) self.num_labels = num_labels self.deberta = DebertaV2Model(config) self.pooler = ContextPooler(config) output_dim = self.pooler.output_dim self.classifier = nn.Linear(output_dim, 1) drop_out = getattr(config, "cls_dropout", None) drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out self.dropout = StableDropout(drop_out) self.init_weights() def get_input_embeddings(self): return self.deberta.get_input_embeddings() def set_input_embeddings(self, new_embeddings): self.deberta.set_input_embeddings(new_embeddings) @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, MultipleChoiceModelOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None flat_inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.deberta( flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids, attention_mask=flat_attention_mask, inputs_embeds=flat_inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) encoder_layer = outputs[0] pooled_output = self.pooler(encoder_layer) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py/0

{ "file_path": "transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py", "repo_id": "transformers", "token_count": 29893 }

296

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert DeiT distilled checkpoints from the timm library.""" import argparse import json from pathlib import Path import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config, base_model=False): rename_keys = [] for i in range(config.num_hidden_layers): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")) rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")) rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")) # projection layer + position embeddings rename_keys.extend( [ ("cls_token", "deit.embeddings.cls_token"), ("dist_token", "deit.embeddings.distillation_token"), ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"), ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"), ("pos_embed", "deit.embeddings.position_embeddings"), ] ) if base_model: # layernorm + pooler rename_keys.extend( [ ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ("pre_logits.fc.weight", "pooler.dense.weight"), ("pre_logits.fc.bias", "pooler.dense.bias"), ] ) # if just the base model, we should remove "deit" from all keys that start with "deit" rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("deit") else pair for pair in rename_keys] else: # layernorm + classification heads rename_keys.extend( [ ("norm.weight", "deit.layernorm.weight"), ("norm.bias", "deit.layernorm.bias"), ("head.weight", "cls_classifier.weight"), ("head.bias", "cls_classifier.bias"), ("head_dist.weight", "distillation_classifier.weight"), ("head_dist.bias", "distillation_classifier.bias"), ] ) return rename_keys # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config, base_model=False): for i in range(config.num_hidden_layers): if base_model: prefix = "" else: prefix = "deit." # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_deit_checkpoint(deit_name, pytorch_dump_folder_path): """ Copy/paste/tweak model's weights to our DeiT structure. """ # define default DeiT configuration config = DeiTConfig() # all deit models have fine-tuned heads base_model = False # dataset (fine-tuned on ImageNet 2012), patch_size and image_size config.num_labels = 1000 repo_id = "huggingface/label-files" filename = "imagenet-1k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} config.patch_size = int(deit_name[-6:-4]) config.image_size = int(deit_name[-3:]) # size of the architecture if deit_name[9:].startswith("tiny"): config.hidden_size = 192 config.intermediate_size = 768 config.num_hidden_layers = 12 config.num_attention_heads = 3 elif deit_name[9:].startswith("small"): config.hidden_size = 384 config.intermediate_size = 1536 config.num_hidden_layers = 12 config.num_attention_heads = 6 if deit_name[9:].startswith("base"): pass elif deit_name[4:].startswith("large"): config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 # load original model from timm timm_model = timm.create_model(deit_name, pretrained=True) timm_model.eval() # load state_dict of original model, remove and rename some keys state_dict = timm_model.state_dict() rename_keys = create_rename_keys(config, base_model) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config, base_model) # load HuggingFace model model = DeiTForImageClassificationWithTeacher(config).eval() model.load_state_dict(state_dict) # Check outputs on an image, prepared by DeiTImageProcessor size = int( (256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103 image_processor = DeiTImageProcessor(size=size, crop_size=config.image_size) encoding = image_processor(images=prepare_img(), return_tensors="pt") pixel_values = encoding["pixel_values"] outputs = model(pixel_values) timm_logits = timm_model(pixel_values) assert timm_logits.shape == outputs.logits.shape assert torch.allclose(timm_logits, outputs.logits, atol=1e-3) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {deit_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--deit_name", default="vit_deit_base_distilled_patch16_224", type=str, help="Name of the DeiT timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)

transformers/src/transformers/models/deit/convert_deit_timm_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/deit/convert_deit_timm_to_pytorch.py", "repo_id": "transformers", "token_count": 3875 }

297

# Copyright 2023 EleutherAI 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. from typing import TYPE_CHECKING from ....utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_open_llama"] = ["LlamaTokenizer"] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_open_llama_fast"] = ["LlamaTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_open_llama"] = [ "OpenLlamaForCausalLM", "OpenLlamaModel", "OpenLlamaPreTrainedModel", "OpenLlamaForSequenceClassification", ] if TYPE_CHECKING: from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from transformers import LlamaTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from transformers import LlamaTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_open_llama import ( OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel, OpenLlamaPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/deprecated/open_llama/__init__.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/open_llama/__init__.py", "repo_id": "transformers", "token_count": 1041 }

298

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Transformer XL checkpoint and datasets.""" import argparse import os import pickle import sys import torch from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl from transformers.models.deprecated.transfo_xl import tokenization_transfo_xl as data_utils from transformers.models.deprecated.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging logging.set_verbosity_info() # We do this to be able to load python 2 datasets pickles # See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918 data_utils.Vocab = data_utils.TransfoXLTokenizer data_utils.Corpus = data_utils.TransfoXLCorpus sys.modules["data_utils"] = data_utils sys.modules["vocabulary"] = data_utils def convert_transfo_xl_checkpoint_to_pytorch( tf_checkpoint_path, transfo_xl_config_file, pytorch_dump_folder_path, transfo_xl_dataset_file ): if transfo_xl_dataset_file: # Convert a pre-processed corpus (see original TensorFlow repo) with open(transfo_xl_dataset_file, "rb") as fp: corpus = pickle.load(fp, encoding="latin1") # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term) pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"] print(f"Save vocabulary to {pytorch_vocab_dump_path}") corpus_vocab_dict = corpus.vocab.__dict__ torch.save(corpus_vocab_dict, pytorch_vocab_dump_path) corpus_dict_no_vocab = corpus.__dict__ corpus_dict_no_vocab.pop("vocab", None) pytorch_dataset_dump_path = pytorch_dump_folder_path + "/" + CORPUS_NAME print(f"Save dataset to {pytorch_dataset_dump_path}") torch.save(corpus_dict_no_vocab, pytorch_dataset_dump_path) if tf_checkpoint_path: # Convert a pre-trained TensorFlow model config_path = os.path.abspath(transfo_xl_config_file) tf_path = os.path.abspath(tf_checkpoint_path) print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.") # Initialise PyTorch model if transfo_xl_config_file == "": config = TransfoXLConfig() else: config = TransfoXLConfig.from_json_file(transfo_xl_config_file) print(f"Building PyTorch model from configuration: {config}") model = TransfoXLLMHeadModel(config) model = load_tf_weights_in_transfo_xl(model, config, tf_path) # Save pytorch-model pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME) pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME) print(f"Save PyTorch model to {os.path.abspath(pytorch_weights_dump_path)}") torch.save(model.state_dict(), pytorch_weights_dump_path) print(f"Save configuration file to {os.path.abspath(pytorch_config_dump_path)}") with open(pytorch_config_dump_path, "w", encoding="utf-8") as f: f.write(config.to_json_string()) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the folder to store the PyTorch model or dataset/vocab.", ) parser.add_argument( "--tf_checkpoint_path", default="", type=str, help="An optional path to a TensorFlow checkpoint path to be converted.", ) parser.add_argument( "--transfo_xl_config_file", default="", type=str, help=( "An optional config json file corresponding to the pre-trained BERT model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--transfo_xl_dataset_file", default="", type=str, help="An optional dataset file to be converted in a vocabulary.", ) args = parser.parse_args() convert_transfo_xl_checkpoint_to_pytorch( args.tf_checkpoint_path, args.transfo_xl_config_file, args.pytorch_dump_folder_path, args.transfo_xl_dataset_file, )

transformers/src/transformers/models/deprecated/transfo_xl/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/deprecated/transfo_xl/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 1991 }

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# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert DETA checkpoints from the original repository. URL: https://github.com/jozhang97/DETA/tree/master""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_download, hf_hub_url from PIL import Image from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_deta_config(): config = DetaConfig( num_queries=900, encoder_ffn_dim=2048, decoder_ffn_dim=2048, num_feature_levels=5, assign_first_stage=True, with_box_refine=True, two_stage=True, ) # set labels config.num_labels = 91 repo_id = "huggingface/label-files" filename = "coco-detection-id2label.json" id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} return config # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # stem # fmt: off rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")) rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")) rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")) rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")) rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")) # stages for stage_idx in range(len(config.backbone_config.depths)): for layer_idx in range(config.backbone_config.depths[stage_idx]): # shortcut if layer_idx == 0: rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var", ) ) # 3 convs for i in range(3): rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean", ) ) rename_keys.append( ( f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var", f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var", ) ) # transformer encoder for i in range(config.encoder_layers): rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", f"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", f"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", f"model.encoder.layers.{i}.self_attn.attention_weights.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", f"model.encoder.layers.{i}.self_attn.attention_weights.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.weight", f"model.encoder.layers.{i}.self_attn.value_proj.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.value_proj.bias", f"model.encoder.layers.{i}.self_attn.value_proj.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.weight", f"model.encoder.layers.{i}.self_attn.output_proj.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.self_attn.output_proj.bias", f"model.encoder.layers.{i}.self_attn.output_proj.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.norm1.weight", f"model.encoder.layers.{i}.self_attn_layer_norm.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"model.encoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"model.encoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"model.encoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"model.encoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"model.encoder.layers.{i}.fc2.bias")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"model.encoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"model.encoder.layers.{i}.final_layer_norm.bias")) # transformer decoder for i in range(config.decoder_layers): rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", f"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", f"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", f"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", f"model.decoder.layers.{i}.encoder_attn.value_proj.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", f"model.decoder.layers.{i}.encoder_attn.value_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", f"model.decoder.layers.{i}.encoder_attn.output_proj.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", f"model.decoder.layers.{i}.encoder_attn.output_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.norm1.weight", f"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"model.decoder.layers.{i}.self_attn.out_proj.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"model.decoder.layers.{i}.self_attn.out_proj.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.norm2.weight", f"model.decoder.layers.{i}.self_attn_layer_norm.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.norm2.bias", f"model.decoder.layers.{i}.self_attn_layer_norm.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"model.decoder.layers.{i}.fc1.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"model.decoder.layers.{i}.fc1.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"model.decoder.layers.{i}.fc2.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"model.decoder.layers.{i}.fc2.bias")) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"model.decoder.layers.{i}.final_layer_norm.weight")) rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"model.decoder.layers.{i}.final_layer_norm.bias")) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def read_in_decoder_q_k_v(state_dict, config): # transformer decoder self-attention layers hidden_size = config.d_model for i in range(config.decoder_layers): # read in weights + bias of input projection layer of self-attention in_proj_weight = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight") in_proj_bias = state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:hidden_size, :] state_dict[f"model.decoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:hidden_size] state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[ hidden_size : hidden_size * 2, : ] state_dict[f"model.decoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[hidden_size : hidden_size * 2] state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-hidden_size:, :] state_dict[f"model.decoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-hidden_size:] # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_deta_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub): """ Copy/paste/tweak model's weights to our DETA structure. """ # load config config = get_deta_config() # load original state dict if model_name == "deta-resnet-50": filename = "adet_checkpoint0011.pth" elif model_name == "deta-resnet-50-24-epochs": filename = "adet_2x_checkpoint0023.pth" else: raise ValueError(f"Model name {model_name} not supported") checkpoint_path = hf_hub_download(repo_id="nielsr/deta-checkpoints", filename=filename) state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_decoder_q_k_v(state_dict, config) # fix some prefixes for key in state_dict.copy().keys(): if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key: val = state_dict.pop(key) state_dict[key.replace("transformer.decoder", "model.decoder")] = val if "input_proj" in key: val = state_dict.pop(key) state_dict["model." + key] = val if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key: val = state_dict.pop(key) state_dict[key.replace("transformer", "model")] = val # finally, create HuggingFace model and load state dict model = DetaForObjectDetection(config) model.load_state_dict(state_dict) model.eval() device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) # load image processor processor = DetaImageProcessor(format="coco_detection") # verify our conversion on image img = prepare_img() encoding = processor(images=img, return_tensors="pt") pixel_values = encoding["pixel_values"] outputs = model(pixel_values.to(device)) # verify logits if model_name == "deta-resnet-50": expected_logits = torch.tensor( [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]] ) expected_boxes = torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]) elif model_name == "deta-resnet-50-24-epochs": expected_logits = torch.tensor( [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]] ) expected_boxes = torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]) assert torch.allclose(outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4) assert torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4) print("Everything ok!") if pytorch_dump_folder_path: # Save model and processor logger.info(f"Saving PyTorch model and processor to {pytorch_dump_folder_path}...") Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) # Push to hub if push_to_hub: print("Pushing model and processor to hub...") model.push_to_hub(f"jozhang97/{model_name}") processor.push_to_hub(f"jozhang97/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_name", type=str, default="deta-resnet-50", choices=["deta-resnet-50", "deta-resnet-50-24-epochs"], help="Name of the model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/deta/convert_deta_resnet_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/deta/convert_deta_resnet_to_pytorch.py", "repo_id": "transformers", "token_count": 7808 }

300

# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert DPT 3.1 checkpoints from the MiDaS repository. URL: https://github.com/isl-org/MiDaS""" import argparse from pathlib import Path import requests import torch from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTImageProcessor, Swinv2Config from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def get_dpt_config(model_name): if "tiny" in model_name: embed_dim = 96 depths = (2, 2, 6, 2) num_heads = (3, 6, 12, 24) window_size = 16 # note: for Swinv2-tiny authors used the window_size = 16 variant # as seen here: https://github.com/isl-org/MiDaS/blob/bdc4ed64c095e026dc0a2f17cabb14d58263decb/midas/backbones/swin2.py#L26 pretrained_window_sizes = (0, 0, 0, 0) elif "base" in model_name: embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) window_size = 24 pretrained_window_sizes = (12, 12, 12, 6) elif "large" in model_name: embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) window_size = 24 pretrained_window_sizes = (12, 12, 12, 6) if "384" in model_name: image_size = 384 elif "256" in model_name: image_size = 256 else: raise ValueError("Model not supported, to do") backbone_config = Swinv2Config( image_size=image_size, embed_dim=embed_dim, depths=depths, window_size=window_size, pretrained_window_sizes=pretrained_window_sizes, num_heads=num_heads, out_features=["stage1", "stage2", "stage3", "stage4"], ) if model_name == "dpt-swinv2-tiny-256": neck_hidden_sizes = [96, 192, 384, 768] elif model_name == "dpt-swinv2-base-384": neck_hidden_sizes = [128, 256, 512, 1024] elif model_name == "dpt-swinv2-large-384": neck_hidden_sizes = [192, 384, 768, 1536] config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes) return config, image_size # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # fmt: off # stem rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight")) rename_keys.append(("pretrained.model.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias")) rename_keys.append(("pretrained.model.patch_embed.norm.weight", "backbone.embeddings.norm.weight")) rename_keys.append(("pretrained.model.patch_embed.norm.bias", "backbone.embeddings.norm.bias")) # transformer encoder for i in range(len(config.backbone_config.depths)): for j in range(config.backbone_config.depths[i]): rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.logit_scale", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.logit_scale")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.2.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.q_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.v_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")) rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")) # downsample parameters if i in [0,1,2]: rename_keys.append((f"pretrained.model.layers.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight")) rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight")) rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias")) # note: non-Transformer backbones like Swinv2, LeViT et al don't require activation postprocessing (readout projections + resize blocks) # refinenet (tricky here) mapping = {1:3, 2:2, 3:1, 4:0} for i in range(1, 5): j = mapping[i] rename_keys.append((f"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight")) rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight")) rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias")) # scratch convolutions for i in range(4): rename_keys.append((f"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight")) # head for i in range(0, 5, 2): rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight")) rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias")) return rename_keys def remove_ignore_keys_(state_dict): ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"] for k in ignore_keys: state_dict.pop(k, None) # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config, model): for i in range(len(config.backbone_config.depths)): for j in range(config.backbone_config.depths[i]): dim = model.backbone.encoder.layers[i].blocks[j].attention.self.all_head_size # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"pretrained.model.layers.{i}.blocks.{j}.attn.qkv.weight") # next, add query, keys and values (in that order) to the state dict state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[ dim : dim * 2, : ] state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[ -dim:, : ] def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, verify_logits, push_to_hub): """ Copy/paste/tweak model's weights to our DPT structure. """ name_to_url = { "dpt-swinv2-tiny-256": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt", "dpt-swinv2-base-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt", "dpt-swinv2-large-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt", } # define DPT configuration based on URL checkpoint_url = name_to_url[model_name] config, image_size = get_dpt_config(model_name) # load original state_dict from URL state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") # load HuggingFace model model = DPTForDepthEstimation(config) # remove certain keys remove_ignore_keys_(state_dict) # rename keys rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) # read in qkv matrices read_in_q_k_v(state_dict, config, model) missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) print("Missing keys:", missing_keys) print("Unexpected keys:", unexpected_keys) model.eval() # Check outputs on an image processor = DPTImageProcessor(size={"height": image_size, "width": image_size}) image = prepare_img() processor(image, return_tensors="pt") if verify_logits: from torchvision import transforms url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) transforms = transforms.Compose( [ transforms.Resize((image_size, image_size)), transforms.ToTensor(), ] ) pixel_values = transforms(image).unsqueeze(0) # forward pass with torch.no_grad(): outputs = model(pixel_values) predicted_depth = outputs.predicted_depth print("Shape of predicted depth:", predicted_depth.shape) print("First values of predicted depth:", predicted_depth[0, :3, :3]) # assert logits if model_name == "dpt-swinv2-base-384": # OK, checked expected_shape = torch.Size([1, 384, 384]) expected_slice = torch.tensor( [ [1998.5575, 1997.3887, 2009.2981], [1952.8607, 1979.6488, 2001.0854], [1953.7697, 1961.7711, 1968.8904], ], ) elif model_name == "dpt-swinv2-tiny-256": # OK, checked expected_shape = torch.Size([1, 256, 256]) expected_slice = torch.tensor( [[978.9163, 976.5215, 978.5349], [974.1859, 971.7249, 975.8046], [971.3419, 970.3118, 971.6830]], ) elif model_name == "dpt-swinv2-large-384": # OK, checked expected_shape = torch.Size([1, 384, 384]) expected_slice = torch.tensor( [ [1203.7206, 1200.1495, 1197.8234], [1196.2484, 1183.5033, 1186.4640], [1178.8131, 1182.3260, 1174.3975], ], ) assert predicted_depth.shape == torch.Size(expected_shape) assert torch.allclose(predicted_depth[0, :3, :3], expected_slice) print("Looks ok!") if pytorch_dump_folder_path is not None: Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model and processor to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) processor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print("Pushing model and processor to hub...") model.push_to_hub(repo_id=f"Intel/{model_name}") processor.push_to_hub(repo_id=f"Intel/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="dpt-swinv2-base-384", type=str, choices=["dpt-swinv2-tiny-256", "dpt-swinv2-base-384", "dpt-swinv2-large-384"], help="Name of the model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--verify_logits", action="store_true", help="Whether to verify logits after conversion.", ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model to the hub after conversion.", ) args = parser.parse_args() convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)

transformers/src/transformers/models/dpt/convert_dpt_swinv2_to_hf.py/0

{ "file_path": "transformers/src/transformers/models/dpt/convert_dpt_swinv2_to_hf.py", "repo_id": "transformers", "token_count": 6687 }

301

# Copyright 2020 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. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"], "tokenization_electra": ["ElectraTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_electra_fast"] = ["ElectraTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_electra"] = [ "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "ElectraForCausalLM", "ElectraForMaskedLM", "ElectraForMultipleChoice", "ElectraForPreTraining", "ElectraForQuestionAnswering", "ElectraForSequenceClassification", "ElectraForTokenClassification", "ElectraModel", "ElectraPreTrainedModel", "load_tf_weights_in_electra", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_electra"] = [ "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFElectraPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_electra"] = [ "FlaxElectraForCausalLM", "FlaxElectraForMaskedLM", "FlaxElectraForMultipleChoice", "FlaxElectraForPreTraining", "FlaxElectraForQuestionAnswering", "FlaxElectraForSequenceClassification", "FlaxElectraForTokenClassification", "FlaxElectraModel", "FlaxElectraPreTrainedModel", ] if TYPE_CHECKING: from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig from .tokenization_electra import ElectraTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_electra_fast import ElectraTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_electra import ( ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ElectraPreTrainedModel, load_tf_weights_in_electra, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_electra import ( TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, TFElectraPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_electra import ( FlaxElectraForCausalLM, FlaxElectraForMaskedLM, FlaxElectraForMultipleChoice, FlaxElectraForPreTraining, FlaxElectraForQuestionAnswering, FlaxElectraForSequenceClassification, FlaxElectraForTokenClassification, FlaxElectraModel, FlaxElectraPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/electra/__init__.py/0

{ "file_path": "transformers/src/transformers/models/electra/__init__.py", "repo_id": "transformers", "token_count": 2221 }

302

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Classes to support Flax Encoder-Decoder architectures""" import os from typing import Optional, Tuple, Union import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput from ...modeling_flax_utils import FlaxPreTrainedModel from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from ..auto.configuration_auto import AutoConfig from ..auto.modeling_flax_auto import FlaxAutoModel, FlaxAutoModelForCausalLM from .configuration_encoder_decoder import EncoderDecoderConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "EncoderDecoderConfig" ENCODER_DECODER_START_DOCSTRING = r""" This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via [`~AutoModel.from_pretrained`] function and the decoder is loaded via [`~AutoModelForCausalLM.from_pretrained`] function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream generative task, like summarization. The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models (see the examples for more information). This model inherits from [`FlaxPreTrainedModel`]. 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 Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Parameters: config ([`EncoderDecoderConfig`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ ENCODER_DECODER_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` 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 [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` 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) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. position_ids (`numpy.ndarray` 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.encoder.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. 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*): If set to `True`, the model will return a [`~utils.FlaxSeq2SeqLMOutput`] instead of a plain tuple. """ ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` 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 [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` 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 (`numpy.ndarray` 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.encoder.max_position_embeddings - 1]`. 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*): If set to `True`, the model will return a [`~utils.FlaxBaseModelOutput`] instead of a plain tuple. """ ENCODER_DECODER_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). For sequence to sequence training, `decoder_input_ids` should be provided. `decoder_input_ids` should be created outside of the model by shifting the `labels` to the right, replacing -100 by the `pad_token_id` and prepending them with the `decoder_start_token_id`. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` 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) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.decoder.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. 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*): If set to `True`, the model will return a [`~utils.FlaxCausalLMOutputWithCrossAttentions`] instead of a plain tuple. """ class FlaxEncoderDecoderModule(nn.Module): config: EncoderDecoderConfig dtype: jnp.dtype = jnp.float32 def setup(self): encoder_config = self.config.encoder decoder_config = self.config.decoder # Copied from `modeling_hybrid_clip.py` with modifications. from ...models.auto.modeling_flax_auto import FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_MAPPING encoder_module = FLAX_MODEL_MAPPING[encoder_config.__class__].module_class decoder_module = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING[decoder_config.__class__].module_class self.encoder = encoder_module(encoder_config, dtype=self.dtype) self.decoder = decoder_module(decoder_config, dtype=self.dtype) # encoder outputs might need to be projected to different dimension for decoder if ( self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None ): self.enc_to_dec_proj = nn.Dense( self.decoder.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.decoder.config.initializer_range), dtype=self.dtype, ) else: self.enc_to_dec_proj = None def _get_encoder_module(self): return self.encoder def _get_projection_module(self): return self.enc_to_dec_proj def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( 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, deterministic=deterministic, ) encoder_hidden_states = encoder_outputs[0] # optionally project encoder_hidden_states if self.enc_to_dec_proj is not None: encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqLMOutput( logits=decoder_outputs.logits, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @add_start_docstrings(ENCODER_DECODER_START_DOCSTRING) class FlaxEncoderDecoderModel(FlaxPreTrainedModel): r""" [`FlaxEncoderDecoderModel`] is a generic model class that will be instantiated as a transformer architecture with the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one as decoder module when created with the :meth*~transformers.FlaxAutoModel.from_pretrained* class method for the encoder and :meth*~transformers.FlaxAutoModelForCausalLM.from_pretrained* class method for the decoder. """ config_class = EncoderDecoderConfig base_model_prefix = "encoder_decoder" module_class = FlaxEncoderDecoderModule def __init__( self, config: EncoderDecoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): if input_shape is None: input_shape = ((1, 1), (1, 1)) if not _do_init: raise ValueError( "`FlaxEncoderDecoderModel` cannot be created without initializing, `_do_init` must be `True`." ) if config.decoder.cross_attention_hidden_size is not None: if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError( "If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal" f" to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for" f" `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for" " `config.encoder.hidden_size`." ) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: encoder_input_shape, decoder_input_shape = input_shape # init input tensors input_ids = jnp.zeros(encoder_input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) decoder_input_ids = jnp.zeros(decoder_input_shape, dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_batch_size, decoder_sequence_length = decoder_input_ids.shape if not decoder_batch_size == batch_size: raise ValueError( f"The inputs of encoder and decoder should have the same batch size, but got {batch_size} for encoder" f" and {decoder_batch_size} for decoder." ) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_sequence_length)[None, :], (decoder_batch_size, decoder_sequence_length) ) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(ENCODER_DECODER_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=_CONFIG_FOR_DOC) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import FlaxEncoderDecoderModel, BertTokenizer >>> # initialize a bert2gpt2 from pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxEncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-cased", "openai-community/gpt2") >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased") >>> text = "My friends are cool but they eat too many carbs." >>> input_ids = tokenizer.encode(text, return_tensors="np") >>> encoder_outputs = model.encode(input_ids) ```""" 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.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) outputs = self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) if return_dict: outputs = FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) return outputs @add_start_docstrings(ENCODER_DECODER_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import FlaxEncoderDecoderModel, BertTokenizer >>> import jax.numpy as jnp >>> # initialize a bert2gpt2 from pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxEncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-cased", "openai-community/gpt2") >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-cased") >>> text = "My friends are cool but they eat too many carbs." >>> input_ids = tokenizer.encode(text, max_length=1024, return_tensors="np") >>> encoder_outputs = model.encode(input_ids) >>> decoder_start_token_id = model.config.decoder.bos_token_id >>> decoder_input_ids = jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" 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.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward( module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states, **kwargs ): projection_module = module._get_projection_module() decoder_module = module._get_decoder_module() # optionally project encoder_hidden_states if projection_module is not None: encoder_hidden_states = projection_module(encoder_hidden_states) return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, encoder_hidden_states=encoder_hidden_states, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs @add_start_docstrings_to_model_forward(ENCODER_DECODER_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Examples: ```python >>> from transformers import FlaxEncoderDecoderModel, BertTokenizer, GPT2Tokenizer >>> # load a fine-tuned bert2gpt2 model >>> model = FlaxEncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16") >>> # load input & output tokenizer >>> tokenizer_input = BertTokenizer.from_pretrained("google-bert/bert-base-cased") >>> tokenizer_output = GPT2Tokenizer.from_pretrained("openai-community/gpt2") >>> article = '''Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members >>> singing a racist chant. SAE's national chapter suspended the students, >>> but University of Oklahoma President David Boren took it a step further, >>> saying the university's affiliation with the fraternity is permanently done.''' >>> input_ids = tokenizer_input(article, add_special_tokens=True, return_tensors="np").input_ids >>> # use GPT2's eos_token as the pad as well as eos token >>> model.config.eos_token_id = model.config.decoder.eos_token_id >>> model.config.pad_token_id = model.config.eos_token_id >>> sequences = model.generate(input_ids, num_beams=4, max_length=12).sequences >>> summary = tokenizer_output.batch_decode(sequences, skip_special_tokens=True)[0] >>> assert summary == "SAS Alpha Epsilon suspended Sigma Alpha Epsilon members" ``` """ 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.return_dict # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: raise ValueError( "`decoder_input_ids` cannot be `None`. For sequence to sequence training, `decoder_position_ids` must" " be specified as an input argument." ) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jax.Array] = None, decoder_attention_mask: Optional[jax.Array] = None, encoder_outputs=None, **kwargs, ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: decoder_position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: decoder_position_ids = jnp.broadcast_to( jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) ) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": decoder_position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs @classmethod def from_encoder_decoder_pretrained( cls, encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, *model_args, **kwargs, ) -> FlaxPreTrainedModel: r""" Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints. Params: encoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*): Information necessary to initiate the encoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. decoder_pretrained_model_name_or_path (`Union[str, os.PathLike]`, *optional*, defaults to `None`): Information necessary to initiate the decoder. Can be either: - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. - A path to a *directory* containing model weights saved using [`~FlaxPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. model_args (remaining positional arguments, *optional*): All remaning positional arguments will be passed to the underlying model's `__init__` method. kwargs (remaining dictionary of keyword arguments, *optional*): Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., `output_attentions=True`). - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter. - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. - To update the parent model configuration, do not use a prefix for each configuration parameter. Behaves differently depending on whether a `config` is provided or automatically loaded. Example: ```python >>> from transformers import FlaxEncoderDecoderModel >>> # initialize a bert2gpt2 from pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized >>> model = FlaxEncoderDecoderModel.from_encoder_decoder_pretrained("google-bert/bert-base-cased", "openai-community/gpt2") >>> # saving model after fine-tuning >>> model.save_pretrained("./bert2gpt2") >>> # load fine-tuned model >>> model = FlaxEncoderDecoderModel.from_pretrained("./bert2gpt2") ```""" kwargs_encoder = { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } kwargs_decoder = { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } # remove encoder, decoder kwargs from kwargs for key in kwargs_encoder.keys(): del kwargs["encoder_" + key] for key in kwargs_decoder.keys(): del kwargs["decoder_" + key] # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: if encoder_pretrained_model_name_or_path is None: raise ValueError( "If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_encoder: encoder_config, kwargs_encoder = AutoConfig.from_pretrained( encoder_pretrained_model_name_or_path, **kwargs_encoder, return_unused_kwargs=True ) if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: logger.info( f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model " "from a decoder model. Cross-attention and casual mask are disabled." ) encoder_config.is_decoder = False encoder_config.add_cross_attention = False kwargs_encoder["config"] = encoder_config encoder = FlaxAutoModel.from_pretrained( encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder ) decoder = kwargs_decoder.pop("model", None) if decoder is None: if decoder_pretrained_model_name_or_path is None: raise ValueError( "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " "to be defined." ) if "config" not in kwargs_decoder: decoder_config, kwargs_decoder = AutoConfig.from_pretrained( decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True ) if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: logger.info( f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." ) decoder_config.is_decoder = True decoder_config.add_cross_attention = True kwargs_decoder["config"] = decoder_config if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning( f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " "passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a " "`decoder_config` to `.from_encoder_decoder_pretrained(...)`" ) decoder = FlaxAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) # instantiate config with corresponding kwargs dtype = kwargs.pop("dtype", jnp.float32) config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs) # init model model = cls(config, dtype=dtype) model.params["encoder"] = encoder.params model.params["decoder"] = decoder.params return model

transformers/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py/0

{ "file_path": "transformers/src/transformers/models/encoder_decoder/modeling_flax_encoder_decoder.py", "repo_id": "transformers", "token_count": 18365 }

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# Copyright 2021 AlQuraishi Laboratory # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import math from functools import partial from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union import torch from .tensor_utils import tensor_tree_map, tree_map def _fetch_dims(tree: Union[dict, list, tuple, torch.Tensor]) -> List[Tuple[int, ...]]: shapes = [] if isinstance(tree, dict): for v in tree.values(): shapes.extend(_fetch_dims(v)) elif isinstance(tree, (list, tuple)): for t in tree: shapes.extend(_fetch_dims(t)) elif isinstance(tree, torch.Tensor): shapes.append(tree.shape) else: raise ValueError("Not supported") return shapes @torch.jit.ignore def _flat_idx_to_idx(flat_idx: int, dims: Tuple[int, ...]) -> Tuple[int, ...]: idx = [] for d in reversed(dims): idx.append(flat_idx % d) flat_idx = flat_idx // d return tuple(reversed(idx)) @torch.jit.ignore def _get_minimal_slice_set( start: Sequence[int], end: Sequence[int], dims: Sequence[int], start_edges: Optional[Sequence[bool]] = None, end_edges: Optional[Sequence[bool]] = None, ) -> List[Tuple[slice, ...]]: """ Produces an ordered sequence of tensor slices that, when used in sequence on a tensor with shape dims, yields tensors that contain every leaf in the contiguous range [start, end]. Care is taken to yield a short sequence of slices, and perhaps even the shortest possible (I'm pretty sure it's the latter). end is INCLUSIVE. """ # start_edges and end_edges both indicate whether, starting from any given # dimension, the start/end index is at the top/bottom edge of the # corresponding tensor, modeled as a tree def reduce_edge_list(l: List[bool]) -> None: tally = True for i in range(len(l)): reversed_idx = -1 * (i + 1) l[reversed_idx] &= tally tally = l[reversed_idx] if start_edges is None: start_edges = [s == 0 for s in start] reduce_edge_list(start_edges) if end_edges is None: end_edges = [e == (d - 1) for e, d in zip(end, dims)] reduce_edge_list(end_edges) # Base cases. Either start/end are empty and we're done, or the final, # one-dimensional tensor can be simply sliced if len(start) == 0: return [()] elif len(start) == 1: return [(slice(start[0], end[0] + 1),)] slices: List[Tuple[slice, ...]] = [] path_list: List[slice] = [] # Dimensions common to start and end can be selected directly for s, e in zip(start, end): if s == e: path_list.append(slice(s, s + 1)) else: break path: Tuple[slice, ...] = tuple(path_list) divergence_idx = len(path) # start == end, and we're done if divergence_idx == len(dims): return [path] def upper() -> Tuple[Tuple[slice, ...], ...]: assert start_edges is not None assert end_edges is not None sdi = start[divergence_idx] return tuple( path + (slice(sdi, sdi + 1),) + s for s in _get_minimal_slice_set( start[divergence_idx + 1 :], [d - 1 for d in dims[divergence_idx + 1 :]], dims[divergence_idx + 1 :], start_edges=start_edges[divergence_idx + 1 :], end_edges=[True for _ in end_edges[divergence_idx + 1 :]], ) ) def lower() -> Tuple[Tuple[slice, ...], ...]: assert start_edges is not None assert end_edges is not None edi = end[divergence_idx] return tuple( path + (slice(edi, edi + 1),) + s for s in _get_minimal_slice_set( [0 for _ in start[divergence_idx + 1 :]], end[divergence_idx + 1 :], dims[divergence_idx + 1 :], start_edges=[True for _ in start_edges[divergence_idx + 1 :]], end_edges=end_edges[divergence_idx + 1 :], ) ) # If both start and end are at the edges of the subtree rooted at # divergence_idx, we can just select the whole subtree at once if start_edges[divergence_idx] and end_edges[divergence_idx]: slices.append(path + (slice(start[divergence_idx], end[divergence_idx] + 1),)) # If just start is at the edge, we can grab almost all of the subtree, # treating only the ragged bottom edge as an edge case elif start_edges[divergence_idx]: slices.append(path + (slice(start[divergence_idx], end[divergence_idx]),)) slices.extend(lower()) # Analogous to the previous case, but the top is ragged this time elif end_edges[divergence_idx]: slices.extend(upper()) slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx] + 1),)) # If both sides of the range are ragged, we need to handle both sides # separately. If there's contiguous meat in between them, we can index it # in one big chunk else: slices.extend(upper()) middle_ground = end[divergence_idx] - start[divergence_idx] if middle_ground > 1: slices.append(path + (slice(start[divergence_idx] + 1, end[divergence_idx]),)) slices.extend(lower()) return slices @torch.jit.ignore def _chunk_slice(t: torch.Tensor, flat_start: int, flat_end: int, no_batch_dims: int) -> torch.Tensor: """ Equivalent to t.reshape((-1,) + t.shape[no_batch_dims:])[flat_start:flat_end] but without the need for the initial reshape call, which can be memory-intensive in certain situations. The only reshape operations in this function are performed on sub-tensors that scale with (flat_end - flat_start), the chunk size. """ batch_dims = t.shape[:no_batch_dims] start_idx = list(_flat_idx_to_idx(flat_start, batch_dims)) # _get_minimal_slice_set is inclusive end_idx = list(_flat_idx_to_idx(flat_end - 1, batch_dims)) # Get an ordered list of slices to perform slices = _get_minimal_slice_set( start_idx, end_idx, batch_dims, ) sliced_tensors = [t[s] for s in slices] return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]) for s in sliced_tensors]) def chunk_layer( layer: Callable, inputs: Dict[str, Any], chunk_size: int, no_batch_dims: int, low_mem: bool = False, _out: Any = None, _add_into_out: bool = False, ) -> Any: """ Implements the "chunking" procedure described in section 1.11.8. Layer outputs and inputs are assumed to be simple "pytrees," consisting only of (arbitrarily nested) lists, tuples, and dicts with torch.Tensor leaves. Args: layer: The layer to be applied chunk-wise inputs: A (non-nested) dictionary of keyworded inputs. All leaves must be tensors and must share the same batch dimensions. chunk_size: The number of sub-batches per chunk. If multiple batch dimensions are specified, a "sub-batch" is defined as a single indexing of all batch dimensions simultaneously (s.t. the number of sub-batches is the product of the batch dimensions). no_batch_dims: How many of the initial dimensions of each input tensor can be considered batch dimensions. low_mem: Avoids flattening potentially large input tensors. Unnecessary in most cases, and is ever so slightly slower than the default setting. Returns: The reassembled output of the layer on the inputs. """ if not (len(inputs) > 0): raise ValueError("Must provide at least one input") initial_dims = [shape[:no_batch_dims] for shape in _fetch_dims(inputs)] orig_batch_dims = tuple([max(s) for s in zip(*initial_dims)]) def _prep_inputs(t: torch.Tensor) -> torch.Tensor: if not low_mem: if not sum(t.shape[:no_batch_dims]) == no_batch_dims: t = t.expand(orig_batch_dims + t.shape[no_batch_dims:]) t = t.reshape(-1, *t.shape[no_batch_dims:]) else: t = t.expand(orig_batch_dims + t.shape[no_batch_dims:]) return t prepped_inputs: Dict[str, Any] = tensor_tree_map(_prep_inputs, inputs) prepped_outputs = None if _out is not None: prepped_outputs = tensor_tree_map(lambda t: t.view([-1] + list(t.shape[no_batch_dims:])), _out) flat_batch_dim = 1 for d in orig_batch_dims: flat_batch_dim *= d no_chunks = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0) def _select_chunk(t: torch.Tensor) -> torch.Tensor: return t[i : i + chunk_size] if t.shape[0] != 1 else t i = 0 out = prepped_outputs for _ in range(no_chunks): # Chunk the input if not low_mem: select_chunk = _select_chunk else: select_chunk = partial( _chunk_slice, flat_start=i, flat_end=min(flat_batch_dim, i + chunk_size), no_batch_dims=len(orig_batch_dims), ) chunks: Dict[str, Any] = tensor_tree_map(select_chunk, prepped_inputs) # Run the layer on the chunk output_chunk = layer(**chunks) # Allocate space for the output if out is None: out = tensor_tree_map(lambda t: t.new_zeros((flat_batch_dim,) + t.shape[1:]), output_chunk) # Put the chunk in its pre-allocated space if isinstance(output_chunk, dict): def assign(d1: dict, d2: dict) -> None: for k, v in d1.items(): if isinstance(v, dict): assign(v, d2[k]) else: if _add_into_out: v[i : i + chunk_size] += d2[k] else: v[i : i + chunk_size] = d2[k] assign(out, output_chunk) elif isinstance(output_chunk, tuple): for x1, x2 in zip(out, output_chunk): if _add_into_out: x1[i : i + chunk_size] += x2 else: x1[i : i + chunk_size] = x2 elif isinstance(output_chunk, torch.Tensor): if _add_into_out: out[i : i + chunk_size] += output_chunk else: out[i : i + chunk_size] = output_chunk else: raise ValueError("Not supported") i += chunk_size out = tensor_tree_map(lambda t: t.view(orig_batch_dims + t.shape[1:]), out) return out class ChunkSizeTuner: def __init__( self, # Heuristically, runtimes for most of the modules in the network # plateau earlier than this on all GPUs I've run the model on. max_chunk_size: int = 512, ): self.max_chunk_size = max_chunk_size self.cached_chunk_size: Optional[int] = None self.cached_arg_data: Optional[tuple] = None def _determine_favorable_chunk_size(self, fn: Callable, args: tuple, min_chunk_size: int) -> int: logging.info("Tuning chunk size...") if min_chunk_size >= self.max_chunk_size: return min_chunk_size candidates: List[int] = [2**l for l in range(int(math.log(self.max_chunk_size, 2)) + 1)] candidates = [c for c in candidates if c > min_chunk_size] candidates = [min_chunk_size] + candidates candidates[-1] += 4 def test_chunk_size(chunk_size: int) -> bool: try: with torch.no_grad(): fn(*args, chunk_size=chunk_size) return True except RuntimeError: return False min_viable_chunk_size_index = 0 i = len(candidates) - 1 while i > min_viable_chunk_size_index: viable = test_chunk_size(candidates[i]) if not viable: i = (min_viable_chunk_size_index + i) // 2 else: min_viable_chunk_size_index = i i = (i + len(candidates) - 1) // 2 return candidates[min_viable_chunk_size_index] def _compare_arg_caches(self, ac1: Iterable, ac2: Iterable) -> bool: consistent = True for a1, a2 in zip(ac1, ac2): assert type(ac1) == type(ac2) if isinstance(ac1, (list, tuple)): consistent &= self._compare_arg_caches(a1, a2) elif isinstance(ac1, dict): a1_items = [v for _, v in sorted(a1.items(), key=lambda x: x[0])] a2_items = [v for _, v in sorted(a2.items(), key=lambda x: x[0])] consistent &= self._compare_arg_caches(a1_items, a2_items) else: consistent &= a1 == a2 return consistent def tune_chunk_size( self, representative_fn: Callable, args: tuple, min_chunk_size: int, ) -> int: consistent = True arg_data: tuple = tree_map(lambda a: a.shape if isinstance(a, torch.Tensor) else a, args, object) if self.cached_arg_data is not None: # If args have changed shape/value, we need to re-tune assert len(self.cached_arg_data) == len(arg_data) consistent = self._compare_arg_caches(self.cached_arg_data, arg_data) else: # Otherwise, we can reuse the precomputed value consistent = False if not consistent: self.cached_chunk_size = self._determine_favorable_chunk_size( representative_fn, args, min_chunk_size, ) self.cached_arg_data = arg_data assert self.cached_chunk_size is not None return self.cached_chunk_size

transformers/src/transformers/models/esm/openfold_utils/chunk_utils.py/0

{ "file_path": "transformers/src/transformers/models/esm/openfold_utils/chunk_utils.py", "repo_id": "transformers", "token_count": 6590 }

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# coding=utf-8 # Copyright 2023 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. """Convert FastSpeech2Conformer HiFi-GAN checkpoint.""" import argparse from pathlib import Path import torch import yaml from transformers import FastSpeech2ConformerHifiGan, FastSpeech2ConformerHifiGanConfig, logging logging.set_verbosity_info() logger = logging.get_logger("transformers.models.FastSpeech2Conformer") def load_weights(checkpoint, hf_model, config): vocoder_key_prefix = "tts.generator.vocoder." checkpoint = {k.replace(vocoder_key_prefix, ""): v for k, v in checkpoint.items() if vocoder_key_prefix in k} hf_model.apply_weight_norm() hf_model.conv_pre.weight_g.data = checkpoint["input_conv.weight_g"] hf_model.conv_pre.weight_v.data = checkpoint["input_conv.weight_v"] hf_model.conv_pre.bias.data = checkpoint["input_conv.bias"] for i in range(len(config.upsample_rates)): hf_model.upsampler[i].weight_g.data = checkpoint[f"upsamples.{i}.1.weight_g"] hf_model.upsampler[i].weight_v.data = checkpoint[f"upsamples.{i}.1.weight_v"] hf_model.upsampler[i].bias.data = checkpoint[f"upsamples.{i}.1.bias"] for i in range(len(config.upsample_rates) * len(config.resblock_kernel_sizes)): for j in range(len(config.resblock_dilation_sizes)): hf_model.resblocks[i].convs1[j].weight_g.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"] hf_model.resblocks[i].convs1[j].weight_v.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"] hf_model.resblocks[i].convs1[j].bias.data = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"] hf_model.resblocks[i].convs2[j].weight_g.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"] hf_model.resblocks[i].convs2[j].weight_v.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"] hf_model.resblocks[i].convs2[j].bias.data = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"] hf_model.conv_post.weight_g.data = checkpoint["output_conv.1.weight_g"] hf_model.conv_post.weight_v.data = checkpoint["output_conv.1.weight_v"] hf_model.conv_post.bias.data = checkpoint["output_conv.1.bias"] hf_model.remove_weight_norm() def remap_hifigan_yaml_config(yaml_config_path): with Path(yaml_config_path).open("r", encoding="utf-8") as f: args = yaml.safe_load(f) args = argparse.Namespace(**args) vocoder_type = args.tts_conf["vocoder_type"] if vocoder_type != "hifigan_generator": raise TypeError(f"Vocoder config must be for `hifigan_generator`, but got {vocoder_type}") remapped_dict = {} vocoder_params = args.tts_conf["vocoder_params"] # espnet_config_key -> hf_config_key key_mappings = { "channels": "upsample_initial_channel", "in_channels": "model_in_dim", "resblock_dilations": "resblock_dilation_sizes", "resblock_kernel_sizes": "resblock_kernel_sizes", "upsample_kernel_sizes": "upsample_kernel_sizes", "upsample_scales": "upsample_rates", } for espnet_config_key, hf_config_key in key_mappings.items(): remapped_dict[hf_config_key] = vocoder_params[espnet_config_key] remapped_dict["sampling_rate"] = args.tts_conf["sampling_rate"] remapped_dict["normalize_before"] = False remapped_dict["leaky_relu_slope"] = vocoder_params["nonlinear_activation_params"]["negative_slope"] return remapped_dict @torch.no_grad() def convert_hifigan_checkpoint( checkpoint_path, pytorch_dump_folder_path, yaml_config_path=None, repo_id=None, ): if yaml_config_path is not None: config_kwargs = remap_hifigan_yaml_config(yaml_config_path) config = FastSpeech2ConformerHifiGanConfig(**config_kwargs) else: config = FastSpeech2ConformerHifiGanConfig() model = FastSpeech2ConformerHifiGan(config) orig_checkpoint = torch.load(checkpoint_path) load_weights(orig_checkpoint, model, config) model.save_pretrained(pytorch_dump_folder_path) if repo_id: print("Pushing to the hub...") model.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") parser.add_argument("--yaml_config_path", default=None, type=str, help="Path to config.yaml of model to convert") parser.add_argument( "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) args = parser.parse_args() convert_hifigan_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.yaml_config_path, args.push_to_hub, )

transformers/src/transformers/models/fastspeech2_conformer/convert_hifigan.py/0

{ "file_path": "transformers/src/transformers/models/fastspeech2_conformer/convert_hifigan.py", "repo_id": "transformers", "token_count": 2201 }

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# coding=utf-8 # Copyright 2022 Meta Platforms authors 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. """ Image/Text processor class for FLAVA """ import warnings from typing import List, Optional, Union from ...image_utils import ImageInput from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy from ...utils import TensorType class FlavaProcessor(ProcessorMixin): r""" Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor. [`FlavaProcessor`] offers all the functionalities of [`FlavaImageProcessor`] and [`BertTokenizerFast`]. See the [`~FlavaProcessor.__call__`] and [`~FlavaProcessor.decode`] for more information. Args: image_processor ([`FlavaImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`BertTokenizerFast`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "FlavaImageProcessor" tokenizer_class = ("BertTokenizer", "BertTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, **kwargs): feature_extractor = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead.", FutureWarning, ) feature_extractor = kwargs.pop("feature_extractor") image_processor = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(image_processor, tokenizer) self.current_processor = self.image_processor def __call__( self, images: Optional[ImageInput] = None, text: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None, add_special_tokens: bool = True, padding: Union[bool, str, PaddingStrategy] = False, truncation: Union[bool, str, TruncationStrategy] = False, max_length: Optional[int] = None, stride: int = 0, pad_to_multiple_of: Optional[int] = None, return_image_mask: Optional[bool] = None, return_codebook_pixels: Optional[bool] = None, return_token_type_ids: Optional[bool] = None, return_attention_mask: Optional[bool] = None, return_overflowing_tokens: bool = False, return_special_tokens_mask: bool = False, return_offsets_mapping: bool = False, return_length: bool = False, verbose: bool = True, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs, ): """ This method uses [`FlavaImageProcessor.__call__`] method to prepare image(s) for the model, and [`BertTokenizerFast.__call__`] to prepare text for the model. Please refer to the docstring of the above two methods for more information. """ if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none.") if text is not None: encoding = self.tokenizer( text=text, add_special_tokens=add_special_tokens, padding=padding, truncation=truncation, max_length=max_length, stride=stride, pad_to_multiple_of=pad_to_multiple_of, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, return_offsets_mapping=return_offsets_mapping, return_length=return_length, verbose=verbose, return_tensors=return_tensors, **kwargs, ) if images is not None: image_features = self.image_processor( images, return_image_mask=return_image_mask, return_codebook_pixels=return_codebook_pixels, return_tensors=return_tensors, **kwargs, ) if text is not None and images is not None: encoding.update(image_features) return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) @property def feature_extractor_class(self): warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", FutureWarning, ) return self.image_processor_class @property def feature_extractor(self): warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", FutureWarning, ) return self.image_processor

transformers/src/transformers/models/flava/processing_flava.py/0

{ "file_path": "transformers/src/transformers/models/flava/processing_flava.py", "repo_id": "transformers", "token_count": 2767 }

306

# Copyright 2020 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. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"], "convert_funnel_original_tf_checkpoint_to_pytorch": [], "tokenization_funnel": ["FunnelTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_funnel_fast"] = ["FunnelTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_funnel"] = [ "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "FunnelBaseModel", "FunnelForMaskedLM", "FunnelForMultipleChoice", "FunnelForPreTraining", "FunnelForQuestionAnswering", "FunnelForSequenceClassification", "FunnelForTokenClassification", "FunnelModel", "FunnelPreTrainedModel", "load_tf_weights_in_funnel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_funnel"] = [ "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFunnelBaseModel", "TFFunnelForMaskedLM", "TFFunnelForMultipleChoice", "TFFunnelForPreTraining", "TFFunnelForQuestionAnswering", "TFFunnelForSequenceClassification", "TFFunnelForTokenClassification", "TFFunnelModel", "TFFunnelPreTrainedModel", ] if TYPE_CHECKING: from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig from .tokenization_funnel import FunnelTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_funnel_fast import FunnelTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_funnel import ( FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, FunnelBaseModel, FunnelForMaskedLM, FunnelForMultipleChoice, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForSequenceClassification, FunnelForTokenClassification, FunnelModel, FunnelPreTrainedModel, load_tf_weights_in_funnel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_funnel import ( TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, TFFunnelBaseModel, TFFunnelForMaskedLM, TFFunnelForMultipleChoice, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForSequenceClassification, TFFunnelForTokenClassification, TFFunnelModel, TFFunnelPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/funnel/__init__.py/0

{ "file_path": "transformers/src/transformers/models/funnel/__init__.py", "repo_id": "transformers", "token_count": 1742 }

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# coding=utf-8 # Copyright 2024 Google Inc., 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. """Flax Gemma model.""" from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp import numpy as np from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_gemma import GemmaConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "GemmaConfig" _CHECKPOINT_FOR_DOC = "google/gemma-2b" _REAL_CHECKPOINT_FOR_DOC = "openlm-research/open_llama_3b_v2" GEMMA_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. 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 Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`GemmaConfig`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16`, or `jax.numpy.bfloat16`. This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ GEMMA_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_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 (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`numpy.ndarray` 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 (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. 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. """ def create_sinusoidal_positions(num_pos, dim): inv_freq = 1.0 / (10000 ** (np.arange(0, dim, 2)[: (dim // 2)] / dim)) freqs = np.einsum("i , j -> i j", np.arange(num_pos), inv_freq).astype("float32") emb = np.concatenate((freqs, freqs), axis=-1) out = np.concatenate((np.sin(emb)[:, None, :], np.cos(emb)[:, None, :]), axis=-1) return jnp.array(out[:, :, :num_pos]) # Copied from transformers.models.llama.modeling_flax_llama.rotate_half def rotate_half(tensor): """Rotates half the hidden dims of the input.""" rotate_half_tensor = jnp.concatenate( (-tensor[..., tensor.shape[-1] // 2 :], tensor[..., : tensor.shape[-1] // 2]), axis=-1 ) return rotate_half_tensor # Copied from transformers.models.llama.modeling_flax_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(tensor, sin_pos, cos_pos): return (tensor * cos_pos) + (rotate_half(tensor) * sin_pos) class FlaxGemmaRMSNorm(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.epsilon = self.config.rms_norm_eps self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size) def __call__(self, hidden_states): variance = jnp.asarray(hidden_states, dtype=jnp.float32) variance = jnp.power(variance, 2) variance = variance.mean(-1, keepdims=True) # use `jax.numpy.sqrt` as `jax.lax.rsqrt` does not match `torch.rsqrt` hidden_states = hidden_states / jnp.sqrt(variance + self.epsilon) return (1 + self.weight) * jnp.asarray(hidden_states, dtype=self.dtype) # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaRotaryEmbedding with Llama->Gemma class FlaxGemmaRotaryEmbedding(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 # Ignore copy def setup(self): head_dim = self.config.head_dim self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim) def __call__(self, key, query, position_ids): sincos = self.sincos[position_ids] sin_pos, cos_pos = jnp.split(sincos, 2, axis=-1) key = apply_rotary_pos_emb(key, sin_pos, cos_pos) query = apply_rotary_pos_emb(query, sin_pos, cos_pos) key = jnp.asarray(key, dtype=self.dtype) query = jnp.asarray(query, dtype=self.dtype) return key, query class FlaxGemmaAttention(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 causal: bool = True is_cross_attention: bool = False def setup(self): config = self.config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = config.head_dim self.attention_softmax_in_fp32 = self.dtype is not jnp.float32 self.num_key_value_heads = config.num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads kernel = jax.nn.initializers.normal(self.config.initializer_range) self.q_proj = nn.Dense( self.num_heads * self.head_dim, use_bias=config.attention_bias, dtype=self.dtype, kernel_init=kernel ) self.k_proj = nn.Dense( self.num_key_value_heads * self.head_dim, use_bias=config.attention_bias, dtype=self.dtype, kernel_init=kernel, ) self.v_proj = nn.Dense( self.num_key_value_heads * self.head_dim, use_bias=config.attention_bias, dtype=self.dtype, kernel_init=kernel, ) self.o_proj = nn.Dense(self.embed_dim, use_bias=config.attention_bias, dtype=self.dtype, kernel_init=kernel) self.causal_mask = make_causal_mask(jnp.ones((1, config.max_position_embeddings), dtype="bool"), dtype="bool") self.rotary_emb = FlaxGemmaRotaryEmbedding(config, dtype=self.dtype) def _split_heads(self, hidden_states, num_heads): return hidden_states.reshape(hidden_states.shape[:2] + (num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads * self.head_dim,)) @nn.compact # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoSelfAttention._concatenate_to_cache def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states, attention_mask, position_ids, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_heads) key = self._split_heads(key, self.num_key_value_heads) value = self._split_heads(value, self.num_key_value_heads) key, query = self.rotary_emb(key, query, position_ids) query_length, key_length = query.shape[1], key.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] batch_size = hidden_states.shape[0] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) dropout_rng = None if not deterministic and self.config.attention_dropout > 0.0: dropout_rng = self.make_rng("dropout") # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.has_variable("cache", "cached_key") or init_cache: key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask) # transform boolean mask into float mask attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) key = jnp.repeat(key, repeats=self.num_key_value_groups, axis=2) value = jnp.repeat(value, repeats=self.num_key_value_groups, axis=2) # usual dot product attention attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype attn_weights = dot_product_attention_weights( query, key, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.config.attention_dropout, deterministic=deterministic, dtype=attention_dtype, ) if self.attention_softmax_in_fp32: attn_weights = attn_weights.astype(self.dtype) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value) attn_output = self._merge_heads(attn_output) attn_output = self.o_proj(attn_output) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs class FlaxGemmaMLP(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim kernel_init = jax.nn.initializers.normal(self.config.initializer_range) if self.config.hidden_activation is None: logger.warning_once( "Gemma's activation function should be approximate GeLU and not exact GeLU. " "Changing the activation function to `gelu_pytorch_tanh`." f"if you want to use the legacy `{self.config.hidden_act}`, " f"edit the `model.config` to set `hidden_activation={self.config.hidden_act}` " " instead of `hidden_act`. See https://github.com/huggingface/transformers/pull/29402 for more details." ) hidden_activation = "gelu_pytorch_tanh" else: hidden_activation = self.config.hidden_activation self.act = ACT2FN[hidden_activation] self.gate_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) self.down_proj = nn.Dense(embed_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) self.up_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init) def __call__(self, hidden_states): up_proj_states = self.up_proj(hidden_states) gate_states = self.act(self.gate_proj(hidden_states)) hidden_states = self.down_proj(up_proj_states * gate_states) return hidden_states # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaDecoderLayer with Llama->Gemma class FlaxGemmaDecoderLayer(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.input_layernorm = FlaxGemmaRMSNorm(self.config, dtype=self.dtype) self.self_attn = FlaxGemmaAttention(self.config, dtype=self.dtype) self.post_attention_layernorm = FlaxGemmaRMSNorm(self.config, dtype=self.dtype) self.mlp = FlaxGemmaMLP(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, ): residual = hidden_states hidden_states = self.input_layernorm(hidden_states) outputs = self.self_attn( hidden_states, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, ) # residual connection attn_output = outputs[0] hidden_states = residual + attn_output residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + hidden_states return (hidden_states,) + outputs[1:] # Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoPreTrainedModel with GPTNeo->Gemma, GPT_NEO->GEMMA, transformer->model class FlaxGemmaPreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GemmaConfig base_model_prefix = "model" module_class: nn.Module = None def __init__( self, config: GemmaConfig, input_shape: Tuple = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length)) attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING) def __call__( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, past_key_values: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): 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.return_dict batch_size, sequence_length = input_ids.shape if position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.") position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGemmaAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, False, output_attentions, output_hidden_states, return_dict, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaLayerCollection with Llama->Gemma class FlaxGemmaLayerCollection(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.blocks = [ FlaxGemmaDecoderLayer(self.config, dtype=self.dtype, name=str(i)) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = False, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for block in self.blocks: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = block( hidden_states, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) # this contains possible `None` values - `FlaxGemmaModule` will filter them out outputs = (hidden_states, all_hidden_states, all_attentions) return outputs # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaModule with Llama->Gemma class FlaxGemmaModule(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.hidden_size = self.config.hidden_size embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range) self.embed_tokens = nn.Embed( self.config.vocab_size, self.hidden_size, embedding_init=embedding_init, dtype=self.dtype, ) self.layers = FlaxGemmaLayerCollection(self.config, dtype=self.dtype) self.norm = FlaxGemmaRMSNorm(self.config, dtype=self.dtype) # Ignore copy def __call__( self, input_ids, attention_mask=None, position_ids=None, deterministic=True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): input_embeds = self.embed_tokens(input_ids.astype("i4")) input_embeds = input_embeds * (self.config.hidden_size**0.5) outputs = self.layers( input_embeds, position_ids=position_ids, attention_mask=attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states = outputs[1] + (hidden_states,) outputs = (hidden_states, all_hidden_states) + outputs[2:] else: outputs = (hidden_states,) + outputs[1:] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=outputs[1], attentions=outputs[-1], ) @add_start_docstrings( "The bare Gemma Model transformer outputting raw hidden-states without any specific head on top.", GEMMA_START_DOCSTRING, ) # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaModel with Llama->Gemma class FlaxGemmaModel(FlaxGemmaPreTrainedModel): module_class = FlaxGemmaModule append_call_sample_docstring( FlaxGemmaModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, ) # Copied from transformers.models.llama.modeling_flax_llama.FlaxLlamaForCausalLMModule with Llama->Gemma class FlaxGemmaForCausalLMModule(nn.Module): config: GemmaConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.model = FlaxGemmaModule(self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range), ) # Ignore copy def __call__( self, input_ids, attention_mask=None, position_ids=None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): outputs = self.model( input_ids, position_ids=position_ids, attention_mask=attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_kernel = self.model.variables["params"]["embed_tokens"]["embedding"].T lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) @add_start_docstrings( """ The Gemma Model transformer with a language modeling head (linear layer) on top. """, GEMMA_START_DOCSTRING, ) # Copied from transformers.models.gptj.modeling_flax_gptj.FlaxGPTJForCausalLM with GPTJ->Gemma class FlaxGemmaForCausalLM(FlaxGemmaPreTrainedModel): module_class = FlaxGemmaForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since Gemma uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxGemmaForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC, real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, )

transformers/src/transformers/models/gemma/modeling_flax_gemma.py/0

{ "file_path": "transformers/src/transformers/models/gemma/modeling_flax_gemma.py", "repo_id": "transformers", "token_count": 13922 }

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# Copyright 2020 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. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_keras_nlp_available, is_tensorflow_text_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = { "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"], "tokenization_gpt2": ["GPT2Tokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_gpt2_fast"] = ["GPT2TokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_gpt2"] = [ "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "GPT2DoubleHeadsModel", "GPT2ForQuestionAnswering", "GPT2ForSequenceClassification", "GPT2ForTokenClassification", "GPT2LMHeadModel", "GPT2Model", "GPT2PreTrainedModel", "load_tf_weights_in_gpt2", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_gpt2"] = [ "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGPT2DoubleHeadsModel", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel", "TFGPT2MainLayer", "TFGPT2Model", "TFGPT2PreTrainedModel", ] try: if not is_keras_nlp_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_gpt2_tf"] = ["TFGPT2Tokenizer"] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_gpt2"] = ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"] if TYPE_CHECKING: from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2OnnxConfig from .tokenization_gpt2 import GPT2Tokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt2_fast import GPT2TokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gpt2 import ( GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2PreTrainedModel, load_tf_weights_in_gpt2, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_gpt2 import ( TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2MainLayer, TFGPT2Model, TFGPT2PreTrainedModel, ) try: if not is_keras_nlp_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt2_tf import TFGPT2Tokenizer try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/gpt2/__init__.py/0

{ "file_path": "transformers/src/transformers/models/gpt2/__init__.py", "repo_id": "transformers", "token_count": 2017 }

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# coding=utf-8 # Copyright 2021 The Eleuther AI and HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch GPT Neo model.""" import os from typing import Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask from ...modeling_outputs import ( BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...pytorch_utils import is_torch_greater_or_equal_than_1_13 from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, is_torch_fx_available, logging, ) from .configuration_gpt_neo import GPTNeoConfig 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 # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph. # It means that the function will not be traced through and simply appear as a node in the graph. if is_torch_fx_available(): if not is_torch_greater_or_equal_than_1_13: import torch.fx _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask) logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "GPTNeoConfig" GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = [ "EleutherAI/gpt-neo-1.3B", # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo ] _CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neo-1.3B" # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) def load_tf_weights_in_gpt_neo(model, config, gpt_neo_checkpoint_path): """Load tf checkpoints in a pytorch model""" try: import re import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(gpt_neo_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: if "global_step" not in name and "adam" not in name: array = tf.train.load_variable(tf_path, name) array = tf.dtypes.cast(array.squeeze(), tf.float32).numpy() name = name.replace("attn/q", "attn/attention/q_proj/w") name = name.replace("attn/k", "attn/attention/k_proj/w") name = name.replace("attn/v", "attn/attention/v_proj/w") name = name.replace("attn/o", "attn/attention/out_proj/w") name = name.replace("norm_1", "ln_1") name = name.replace("norm_2", "ln_2") name = name.replace("attn/compute_output_bias/o_b", "attn/attention/out_proj/b") name = name.replace("conv1d_main/c_fc/kernel", "c_fc/w") name = name.replace("conv1d_main/c_fc/bias", "c_fc/b") name = name.replace("conv1d_main/c_proj/kernel", "c_proj/w") name = name.replace("conv1d_main/c_proj/bias", "c_proj/b") names.append(name) arrays.append(array) for name, array in zip(names, arrays): name = name[5:] # skip "gpt2/" name = name.split("/") pointer = model.transformer for m_name in name: if re.fullmatch(r"[A-Za-z]+\d+", m_name): scope_names = re.split(r"(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "w" or scope_names[0] == "g": pointer = getattr(pointer, "weight") elif scope_names[0] == "b": pointer = getattr(pointer, "bias") elif scope_names[0] == "wpe" or scope_names[0] == "wte": pointer = getattr(pointer, scope_names[0]) pointer = getattr(pointer, "weight") else: pointer = getattr(pointer, scope_names[0]) if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if name[-1] == "w" and name[-2] in ["out_proj", "k_proj", "q_proj", "v_proj", "c_proj", "c_fc"]: array = array.transpose() if name == ["wte"]: # if vocab is padded, then trim off the padding embeddings array = array[: config.vocab_size] if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched {name}") print(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) # init the final linear layer using word embeddings embs = model.transformer.wte.weight lin = nn.Linear(embs.size()[1], embs.size()[0], bias=False) lin.weight = embs model.set_output_embeddings(lin) return model class GPTNeoSelfAttention(nn.Module): def __init__(self, config, attention_type): super().__init__() self.config = config max_positions = config.max_position_embeddings bias = torch.tril(torch.ones((max_positions, max_positions), dtype=bool)).view( 1, 1, max_positions, max_positions ) # local causal self attention is a sliding window where each token can only attend to the previous # window_size tokens. This is implemented by updating the causal mask such that for each token # all other tokens are masked except the previous window_size tokens. if attention_type == "local": bias = torch.bitwise_xor(bias, torch.tril(bias, -config.window_size)) self.register_buffer("bias", bias, persistent=False) self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) self.attn_dropout = nn.Dropout(float(config.attention_dropout)) self.resid_dropout = nn.Dropout(float(config.resid_dropout)) self.is_causal = True self.embed_dim = config.hidden_size self.num_heads = config.num_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.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) def _split_heads(self, tensor, num_heads, attn_head_size): """ Splits hidden_size dim into attn_head_size and num_heads """ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) tensor = tensor.view(new_shape) return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def _merge_heads(self, tensor, num_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden_size """ tensor = tensor.permute(0, 2, 1, 3).contiguous() new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) return tensor.view(new_shape) def _attn(self, query, key, value, attention_mask=None, head_mask=None): # Keep the attention weights computation in fp32 to avoid overflow issues query = query.to(torch.float32) key = key.to(torch.float32) attn_weights = torch.matmul(query, key.transpose(-1, -2)) query_length, key_length = query.size(-2), key.size(-2) causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] mask_value = torch.finfo(attn_weights.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) attn_weights = torch.where(causal_mask, attn_weights, mask_value) if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1) attn_weights = attn_weights.to(value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def forward( self, hidden_states, attention_mask=None, layer_past=None, head_mask=None, use_cache=False, output_attentions=False, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key = layer_past[0] past_value = layer_past[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) if use_cache is True: present = (key, value) else: present = None attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs # a, present, (attentions) class GPTNeoFlashAttention2(GPTNeoSelfAttention): """ GPTNeo flash attention module. This module inherits from `GPTNeoSelfAttention` 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, attention_mask=None, layer_past=None, head_mask=None, use_cache=False, output_attentions=False, ): bsz, _, _ = hidden_states.size() query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, self.num_heads, self.head_dim) key = self._split_heads(key, self.num_heads, self.head_dim) value = self._split_heads(value, self.num_heads, self.head_dim) if layer_past is not None: past_key = layer_past[0] past_value = layer_past[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) if use_cache is True: present = (key, value) else: present = None query_length = query.shape[2] tgt_len = key.shape[2] # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim query = query.transpose(1, 2).view(bsz, query_length, self.num_heads, self.head_dim) key = key.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) value = value.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim) attn_dropout = self.config.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. (LlamaRMSNorm handles it correctly) if query.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query = query.to(target_dtype) key = key.to(target_dtype) value = value.to(target_dtype) attn_output = self._flash_attention_forward( query, key, value, attention_mask, query_length, dropout=attn_dropout, softmax_scale=1.0 ) attn_weights_reshaped = attn_output.reshape(bsz, query_length, self.num_heads * self.head_dim) attn_output = self.out_proj(attn_weights_reshaped) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights_reshaped,) return outputs # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`float`): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) """ if not self._flash_attn_uses_top_left_mask: causal = self.is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. causal = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) GPT_NEO_ATTENTION_CLASSES = { "eager": GPTNeoSelfAttention, "flash_attention_2": GPTNeoFlashAttention2, } class GPTNeoAttention(nn.Module): def __init__(self, config, layer_id=0): super().__init__() self.layer_id = layer_id self.attention_layers = config.attention_layers self.attention_type = self.attention_layers[layer_id] if self.attention_type in ["global", "local"]: self.attention = GPT_NEO_ATTENTION_CLASSES[config._attn_implementation](config, self.attention_type) else: raise NotImplementedError( "Only attn layer types 'global' and 'local' exist, but got `config.attention_layers`: " f"{config.attention_layers}. Select attn layer types from ['global', 'local'] only." ) def forward( self, hidden_states, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, ): return self.attention( hidden_states, attention_mask=attention_mask, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) class GPTNeoMLP(nn.Module): def __init__(self, intermediate_size, config): # in MLP: intermediate_size= 4 * hidden_size super().__init__() embed_dim = config.hidden_size self.c_fc = nn.Linear(embed_dim, intermediate_size) self.c_proj = nn.Linear(intermediate_size, embed_dim) self.act = ACT2FN[config.activation_function] self.dropout = nn.Dropout(float(config.resid_dropout)) def forward(self, hidden_states): hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class GPTNeoBlock(nn.Module): def __init__(self, config, layer_id): super().__init__() hidden_size = config.hidden_size inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.attn = GPTNeoAttention(config, layer_id) self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon) self.mlp = GPTNeoMLP(inner_dim, config) def forward( self, hidden_states, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False, ): residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attn_outputs[0] # output_attn: a, present, (attentions) outputs = attn_outputs[1:] # residual connection hidden_states = attn_output + residual residual = hidden_states hidden_states = self.ln_2(hidden_states) feed_forward_hidden_states = self.mlp(hidden_states) # residual connection hidden_states = residual + feed_forward_hidden_states if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions, cross_attentions) class GPTNeoPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTNeoConfig load_tf_weights = load_tf_weights_in_gpt_neo base_model_prefix = "transformer" supports_gradient_checkpointing = True _no_split_modules = ["GPTNeoBlock"] _skip_keys_device_placement = "past_key_values" _supports_flash_attn_2 = True def __init__(self, *inputs, **kwargs): super().__init__(*inputs, **kwargs) def _init_weights(self, module): """Initialize the weights.""" if isinstance(module, (nn.Linear,)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) GPT_NEO_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 ([`GPTNeoConfig`]): 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. """ GPT_NEO_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past_key_values` is `None` else `past_key_values[0][0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`Tuple[Tuple[torch.Tensor]]` of length `config.num_layers`): Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see `past_key_values` output below). Can be used to speed up sequential decoding. The `input_ids` which have their past given to this model should not be passed as `input_ids` as they have already been computed. attention_mask (`torch.FloatTensor` 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) token_type_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) 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) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see `past_key_values`). use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare GPT Neo Model transformer outputting raw hidden-states without any specific head on top.", GPT_NEO_START_DOCSTRING, ) class GPTNeoModel(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.embed_dim = config.hidden_size self.wte = nn.Embedding(config.vocab_size, self.embed_dim) self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) self.drop = nn.Dropout(float(config.embed_dropout)) self.h = nn.ModuleList([GPTNeoBlock(config, layer_id=i) for i in range(config.num_layers)]) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.wte def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: 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, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]: 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 not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) if past_key_values is None: past_length = 0 past_key_values = tuple([None] * len(self.h)) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x num_heads x N x N # head_mask has shape n_layer x batch x num_heads x N x N head_mask = self.get_head_mask(head_mask, self.config.num_layers) if inputs_embeds is None: inputs_embeds = self.wte(input_ids) position_embeds = self.wpe(position_ids) hidden_states = inputs_embeds + position_embeds # Attention mask. if self._use_flash_attention_2: # 2d mask is passed through the layers attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None else: # 4d mask is passed through the layers attention_mask = _prepare_4d_causal_attention_mask(attention_mask, input_shape, inputs_embeds, past_length) if token_type_ids is not None: token_type_embeds = self.wte(token_type_ids) hidden_states = hidden_states + token_type_embeds hidden_states = self.drop(hidden_states) output_shape = (-1,) + input_shape[1:] + (hidden_states.size(-1),) 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 presents = () if use_cache else None all_self_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: outputs = self._gradient_checkpointing_func( block.__call__, hidden_states, None, attention_mask, head_mask[i], use_cache, output_attentions, ) else: outputs = block( hidden_states, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.ln_f(hidden_states) hidden_states = hidden_states.view(output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions, ) @add_start_docstrings( """ The GPT Neo Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForCausalLM(GPTNeoPreTrainedModel): _tied_weights_keys = ["lm_head.weight"] def __init__(self, config): super().__init__(config) self.transformer = GPTNeoModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # Omit tokens covered by past_key_values if past_key_values: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] if token_type_ids is not None: token_type_ids = token_type_ids[:, -input_ids.shape[1] :] attention_mask = kwargs.get("attention_mask", None) position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "position_ids": position_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } ) return model_inputs @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_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, ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_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 = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # move labels to correct device to enable model parallelism labels = labels.to(lm_logits.device) # Compute loss in fp32 to match with mesh-tf version # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179 lm_logits = lm_logits.to(torch.float32) # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) lm_logits = lm_logits.to(hidden_states.dtype) loss = loss.to(hidden_states.dtype) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @staticmethod def _reorder_cache( past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor ) -> Tuple[Tuple[torch.Tensor]]: """ This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or [`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct beam_idx at every generation step. """ return tuple( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) for layer_past in past_key_values ) @add_start_docstrings( """ The GPTNeo Model transformer with a sequence classification head on top (linear layer). [`GPTNeoForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT-1) 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). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForSequenceClassification(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[torch.FloatTensor]] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_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, ) -> Union[Tuple[torch.Tensor], 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 transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_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 = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] 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 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ GPT Neo model 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. """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForTokenClassification(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.dropout = nn.Dropout(config.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(GPT_NEO_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint="EleutherAI/gpt-neo-125m", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_loss=0.25, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[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, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *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 transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_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 = transformer_outputs[0] hidden_states = self.dropout(hidden_states) logits = self.classifier(hidden_states) loss = None if labels is not None: labels = labels.to(logits.device) loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) @add_start_docstrings( """ The GPT-Neo Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPT_NEO_START_DOCSTRING, ) class GPTNeoForQuestionAnswering(GPTNeoPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPTNeoModel(config) self.qa_outputs = nn.Linear(config.hidden_size, 2) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(GPT_NEO_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, real_checkpoint=_CHECKPOINT_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, QuestionAnsweringModelOutput]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.transformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1).contiguous() end_logits = end_logits.squeeze(-1).contiguous() total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/gpt_neo/modeling_gpt_neo.py/0

{ "file_path": "transformers/src/transformers/models/gpt_neo/modeling_gpt_neo.py", "repo_id": "transformers", "token_count": 25685 }

310

# coding=utf-8 # Copyright 2022 The EleutherAI and HuggingFace Teams. 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. """ TF 2.0 GPT-J model.""" from __future__ import annotations from typing import Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...file_utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, ) from ...modeling_tf_outputs import ( TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutputWithPast, ) from ...modeling_tf_utils import ( TFCausalLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSharedEmbeddings, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import logging from .configuration_gptj import GPTJConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B" _CONFIG_FOR_DOC = "GPTJConfig" GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = [ "EleutherAI/gpt-j-6B", # See all GPT-J models at https://huggingface.co/models?filter=gptj ] def create_sinusoidal_positions(num_pos: int, dim: int) -> tf.Tensor: inv_freq = tf.cast(1.0 / (10000 ** (tf.range(0, dim, 2) / dim)), tf.float32) sinusoid_inp = tf.cast(tf.einsum("i , j -> i j", tf.range(num_pos, dtype=tf.float32), inv_freq), tf.float32) sin, cos = tf.sin(sinusoid_inp), tf.cos(sinusoid_inp) out = tf.concat((sin, cos), axis=1) return out def rotate_every_two(x: tf.Tensor) -> tf.Tensor: rotate_half_tensor = tf.stack((-x[:, :, :, 1::2], x[:, :, :, ::2]), axis=-1) new_shape = shape_list(rotate_half_tensor)[:-2] + [tf.math.reduce_prod(shape_list(rotate_half_tensor)[-2:])] rotate_half_tensor = tf.reshape(rotate_half_tensor, new_shape) return rotate_half_tensor def apply_rotary_pos_emb(tensor: tf.Tensor, sincos: tf.Tensor) -> tf.Tensor: sin_pos, cos_pos = sincos sin_pos = tf.repeat(sin_pos[:, :, None, :], 2, 3) cos_pos = tf.repeat(cos_pos[:, :, None, :], 2, 3) return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos) class TFGPTJAttention(keras.layers.Layer): def __init__(self, config: GPTJConfig, **kwargs): super().__init__(**kwargs) self.embed_dim = config.hidden_size self.num_attention_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_attention_heads if self.head_dim * self.num_attention_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and" f" `num_attention_heads`: {self.num_attention_heads})." ) self.scale_attn = self.head_dim**0.5 self.rotary_dim = config.rotary_dim self.attn_dropout = keras.layers.Dropout(config.attn_pdrop) self.resid_dropout = keras.layers.Dropout(config.resid_pdrop) self.q_proj = keras.layers.Dense( self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="q_proj", ) self.k_proj = keras.layers.Dense( self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="k_proj", ) self.v_proj = keras.layers.Dense( self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="v_proj", ) self.out_proj = keras.layers.Dense( self.embed_dim, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="out_proj", ) self.max_positions = config.max_position_embeddings self.lower_triangle_mask = tf.reshape( tf.cast(tf.experimental.numpy.tril(tf.ones((self.max_positions, self.max_positions))), tf.int8), (1, 1, self.max_positions, self.max_positions), ) pos_embd_dim = self.rotary_dim or self.embed_dim self.embed_positions = create_sinusoidal_positions(self.max_positions, pos_embd_dim) def get_causal_mask(self, key_length, query_length) -> tf.Tensor: return tf.cast(self.lower_triangle_mask[:, :, key_length - query_length : key_length, :key_length], tf.bool) @staticmethod def get_masked_bias(dtype: tf.DType) -> tf.Tensor: return tf.cast(tf.constant(-1e9), dtype) def _split_heads(self, hidden_states: tf.Tensor, rotary: bool) -> tf.Tensor: """ Splits hidden dim into attn_head_size and num_attention_heads """ new_shape = shape_list(hidden_states)[:-1] + [self.num_attention_heads, self.head_dim] hidden_states = tf.reshape(hidden_states, new_shape) if rotary: return hidden_states if len(shape_list(hidden_states)) == 4: return tf.transpose(hidden_states, (0, 2, 1, 3)) # (batch, head, seq_length, head_features) if len(shape_list(hidden_states)) == 5: return tf.transpose(hidden_states, (0, 1, 3, 2, 4)) # (batch, blocks, head, block_length, head_features) raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}") def _merge_heads(self, hidden_states: tf.Tensor) -> tf.Tensor: """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ if len(shape_list(hidden_states)) == 4: hidden_states = tf.transpose(hidden_states, (0, 2, 1, 3)) elif len(shape_list(hidden_states)) == 5: hidden_states = tf.transpose(hidden_states, (0, 1, 3, 2, 4)) else: raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}") new_shape = shape_list(hidden_states)[:-2] + [self.num_attention_heads * self.head_dim] return tf.reshape(hidden_states, new_shape) def _attn( self, query: tf.Tensor, key: tf.Tensor, value: tf.Tensor, attention_mask: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, ) -> Tuple[tf.Tensor, tf.Tensor]: # compute causal mask from causal mask buffer query_length, key_length = shape_list(query)[-2], shape_list(key)[-2] causal_mask = self.get_causal_mask(key_length, query_length) # Keep the attention weights computation in fp32 to avoid overflow issues query = tf.cast(query, tf.float32) key = tf.cast(key, tf.float32) attn_weights = tf.matmul(query, key, transpose_b=True) attn_weights = tf.where(causal_mask, attn_weights, self.get_masked_bias(attn_weights.dtype)) attn_weights = attn_weights / self.scale_attn if attention_mask is not None: # Apply the attention mask attn_weights = attn_weights + attention_mask attn_weights = stable_softmax(attn_weights, axis=-1) attn_weights = tf.cast(attn_weights, value.dtype) attn_weights = self.attn_dropout(attn_weights) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = tf.matmul(attn_weights, value) return attn_output, attn_weights def call( self, hidden_states: tf.Tensor, layer_past: Optional[Tuple[tf.Tensor, tf.Tensor]] = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, use_cache: bool = False, output_attentions: bool = False, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query, True) key = self._split_heads(key, True) value = self._split_heads(value, False) sincos = tf.cast(tf.gather(self.embed_positions, position_ids, axis=0), hidden_states.dtype) sincos = tf.split(sincos, 2, axis=-1) if self.rotary_dim is not None: k_rot = key[:, :, :, : self.rotary_dim] k_pass = key[:, :, :, self.rotary_dim :] q_rot = query[:, :, :, : self.rotary_dim] q_pass = query[:, :, :, self.rotary_dim :] k_rot = apply_rotary_pos_emb(k_rot, sincos) q_rot = apply_rotary_pos_emb(q_rot, sincos) key = tf.concat((k_rot, k_pass), axis=-1) query = tf.concat((q_rot, q_pass), axis=-1) else: key = apply_rotary_pos_emb(key, sincos) query = apply_rotary_pos_emb(query, sincos) key = tf.transpose(key, (0, 2, 1, 3)) query = tf.transpose(query, (0, 2, 1, 3)) if layer_past is not None: past_key = layer_past[0] past_value = layer_past[1] key = tf.concat((past_key, key), axis=-2) value = tf.concat((past_value, value), axis=-2) if use_cache is True: present = (key, value) else: present = None # compute self-attention: V x Softmax(QK^T) attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) attn_output = self.resid_dropout(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs # a, present, (attentions) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "q_proj", None) is not None: with tf.name_scope(self.q_proj.name): self.q_proj.build([None, None, self.embed_dim]) if getattr(self, "k_proj", None) is not None: with tf.name_scope(self.k_proj.name): self.k_proj.build([None, None, self.embed_dim]) if getattr(self, "v_proj", None) is not None: with tf.name_scope(self.v_proj.name): self.v_proj.build([None, None, self.embed_dim]) if getattr(self, "out_proj", None) is not None: with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.embed_dim]) class TFGPTJMLP(keras.layers.Layer): def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs): super().__init__(**kwargs) embed_dim = config.n_embd self.fc_in = keras.layers.Dense( intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="fc_in" ) self.fc_out = keras.layers.Dense( embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="fc_out" ) self.act = get_tf_activation(config.activation_function) self.dropout = keras.layers.Dropout(config.embd_pdrop) self.embed_dim = config.n_embd self.intermediate_size = intermediate_size def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.fc_in(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.fc_out(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "fc_in", None) is not None: with tf.name_scope(self.fc_in.name): self.fc_in.build([None, None, self.embed_dim]) if getattr(self, "fc_out", None) is not None: with tf.name_scope(self.fc_out.name): self.fc_out.build([None, None, self.intermediate_size]) class TFGPTJBlock(keras.layers.Layer): def __init__(self, config: GPTJConfig, **kwargs): super().__init__(**kwargs) inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1") self.attn = TFGPTJAttention(config, name="attn") self.mlp = TFGPTJMLP(inner_dim, config, name="mlp") self.config = config def call( self, hidden_states: tf.Tensor, layer_past: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, use_cache: bool = False, output_attentions: bool = False, ): residual = hidden_states hidden_states = self.ln_1(hidden_states) attn_outputs = self.attn( hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) # attn_outputs: attn_output, present, (attentions) attn_output = attn_outputs[0] outputs = attn_outputs[1:] feed_forward_hidden_states = self.mlp(hidden_states) hidden_states = attn_output + feed_forward_hidden_states + residual if use_cache: outputs = (hidden_states,) + outputs else: outputs = (hidden_states,) + outputs[1:] return outputs # hidden_states, present, (attentions) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "ln_1", None) is not None: with tf.name_scope(self.ln_1.name): self.ln_1.build([None, None, self.config.n_embd]) if getattr(self, "attn", None) is not None: with tf.name_scope(self.attn.name): self.attn.build(None) if getattr(self, "mlp", None) is not None: with tf.name_scope(self.mlp.name): self.mlp.build(None) @keras_serializable class TFGPTJMainLayer(keras.layers.Layer): config_class = GPTJConfig def __init__(self, config: GPTJConfig, *inputs, **kwargs): super().__init__(*inputs, **kwargs) self.config = config self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.use_cache = config.use_cache self.return_dict = config.use_return_dict self.num_hidden_layers = config.n_layer self.n_embd = config.n_embd self.n_positions = config.n_positions self.initializer_range = config.initializer_range self.wte = TFSharedEmbeddings( config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte" ) self.drop = keras.layers.Dropout(config.embd_pdrop) self.h = [TFGPTJBlock(config, name=f"h_._{i}") for i in range(config.n_layer)] self.ln_f = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f") self.embed_dim = config.n_embd def get_input_embeddings(self): return self.wte def set_input_embeddings(self, value: tf.Tensor): self.wte.weight = value self.wte.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ raise NotImplementedError @unpack_inputs def call( self, input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, ) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]: if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) input_ids = tf.reshape(input_ids, [-1, input_shape[-1]]) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past_key_values is None: past_length = 0 past_key_values = [None] * len(self.h) else: past_length = shape_list(past_key_values[0][0])[-2] if position_ids is None: position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0) if attention_mask is not None: # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. attention_mask_shape = shape_list(attention_mask) attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. one_cst = tf.constant(1.0) attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype) attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0)) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.num_hidden_layers # head_mask = tf.constant([0] * self.num_hidden_layers) position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]]) if inputs_embeds is None: check_embeddings_within_bounds(input_ids, self.wte.vocab_size) inputs_embeds = self.wte(input_ids, mode="embedding") if token_type_ids is not None: token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]]) token_type_embeds = self.wte(token_type_ids, mode="embedding") else: token_type_embeds = tf.constant(0.0) token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype) hidden_states = inputs_embeds + token_type_embeds hidden_states = self.drop(hidden_states, training=training) output_shape = input_shape + [shape_list(hidden_states)[-1]] presents = () if use_cache else None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),) outputs = block( hidden_states=hidden_states, layer_past=layer_past, attention_mask=attention_mask, position_ids=position_ids, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, training=training, ) hidden_states = outputs[0] if use_cache: presents = presents + (outputs[1],) if output_attentions: all_attentions = all_attentions + (outputs[2 if use_cache else 1],) hidden_states = self.ln_f(hidden_states) hidden_states = tf.reshape(hidden_states, output_shape) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if output_attentions: # let the number of heads free (-1) so we can extract attention even after head pruning attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:] all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions) if not return_dict: return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) return TFBaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "wte", None) is not None: with tf.name_scope(self.wte.name): self.wte.build(None) if getattr(self, "ln_f", None) is not None: with tf.name_scope(self.ln_f.name): self.ln_f.build([None, None, self.embed_dim]) if getattr(self, "h", None) is not None: for layer in self.h: with tf.name_scope(layer.name): layer.build(None) class TFGPTJPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = GPTJConfig base_model_prefix = "transformer" # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias"] GPTJ_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`GPTJConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ GPTJ_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`): `input_ids_length` = `sequence_length` if `past` is `None` else `past[0].shape[-2]` (`sequence_length` of input past key value states). Indices of input sequence tokens in the vocabulary. If `past` is used, only input IDs that do not have their past calculated should be passed as `input_ids`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) past_key_values (`List[tf.Tensor]` of length `config.n_layers`): Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see `past` output below). Can be used to speed up sequential decoding. The token ids which have their past given to this model should not be passed as input ids as they have already been computed. attention_mask (`tf.Tensor` or `Numpy array` 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) token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`tf.Tensor` or `Numpy array` 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) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. 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. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.", GPTJ_START_DOCSTRING, ) class TFGPTJModel(TFGPTJPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPTJMainLayer(config, name="transformer") @unpack_inputs @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]: r""" use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past`). Set to `False` during training, `True` during generation """ outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "transformer", None) is not None: with tf.name_scope(self.transformer.name): self.transformer.build(None) @add_start_docstrings( """ The GPT-J Model transformer with a language modeling head on top. """, GPTJ_START_DOCSTRING, ) class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFGPTJMainLayer(config, name="transformer") self.lm_head = keras.layers.Dense( config.vocab_size, kernel_initializer=get_initializer(config.initializer_range), name="lm_head" ) self.config = config def get_output_embeddings(self): return self.lm_head def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs): token_type_ids = kwargs.get("token_type_ids", None) # only last token for inputs_ids if past is defined in kwargs if past_key_values: inputs = tf.expand_dims(inputs[:, -1], -1) if token_type_ids is not None: token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1) position_ids = kwargs.get("position_ids", None) attention_mask = kwargs.get("attention_mask", None) if attention_mask is not None and position_ids is None: position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True) if past_key_values: position_ids = tf.expand_dims(position_ids[:, -1], -1) return { "input_ids": inputs, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": use_cache, "token_type_ids": token_type_ids, } @unpack_inputs @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, labels: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFCausalLMOutputWithPast, Tuple[tf.Tensor]]: r""" labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # shift labels to the left and cut last logit token shifted_logits = lm_logits[:, :-1] labels = labels[:, 1:] loss = self.hf_compute_loss(labels, shifted_logits) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "transformer", None) is not None: with tf.name_scope(self.transformer.name): self.transformer.build(None) if getattr(self, "lm_head", None) is not None: with tf.name_scope(self.lm_head.name): self.lm_head.build([None, None, self.config.n_embd]) @add_start_docstrings( """ The GPT-J Model transformer with a sequence classification head on top (linear layer). [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models (e.g. GPT, GPT-2, GPT-Neo) 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). """, GPTJ_START_DOCSTRING, ) class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassificationLoss): _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.transformer = TFGPTJMainLayer(config, name="transformer") self.score = keras.layers.Dense( self.num_labels, use_bias=False, kernel_initializer=get_initializer(config.initializer_range), name="score", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, labels: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]: r""" labels (`np.ndarray` or `tf.Tensor` 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). """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states) logits_shape = shape_list(logits) in_logits = None if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = ( tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1) - 1 ) sequence_lengths = tf.where( sequence_lengths >= 0, sequence_lengths, tf.cast(shape_list(input_ids[-1]), sequence_lengths.dtype) - 1, ) in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1) else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) loss = None if labels is not None: if self.config.pad_token_id is None and logits_shape[0] != 1: raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") if not tf.is_tensor(sequence_lengths): in_logits = logits[0 : logits_shape[0], sequence_lengths] loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels])) pooled_logits = in_logits if in_logits is not None else logits if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "transformer", None) is not None: with tf.name_scope(self.transformer.name): self.transformer.build(None) if getattr(self, "score", None) is not None: with tf.name_scope(self.score.name): self.score.build([None, None, self.config.n_embd]) @add_start_docstrings( """ The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, GPTJ_START_DOCSTRING, ) class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss): _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.transformer = TFGPTJMainLayer(config, name="transformer") self.qa_outputs = keras.layers.Dense( self.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids: TFModelInputType | None = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ transformer_outputs = self.transformer( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = transformer_outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels, (start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + transformer_outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "transformer", None) is not None: with tf.name_scope(self.transformer.name): self.transformer.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/gptj/modeling_tf_gptj.py/0

{ "file_path": "transformers/src/transformers/models/gptj/modeling_tf_gptj.py", "repo_id": "transformers", "token_count": 21162 }

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# coding=utf-8 # Copyright 2022 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. """Image processor class for Idefics.""" from typing import Callable, Dict, List, Optional, Union from PIL import Image from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_torch_available IDEFICS_STANDARD_MEAN = [0.48145466, 0.4578275, 0.40821073] IDEFICS_STANDARD_STD = [0.26862954, 0.26130258, 0.27577711] def convert_to_rgb(image): # `image.convert("RGB")` would only work for .jpg images, as it creates a wrong background # for transparent images. The call to `alpha_composite` handles this case if image.mode == "RGB": return image image_rgba = image.convert("RGBA") background = Image.new("RGBA", image_rgba.size, (255, 255, 255)) alpha_composite = Image.alpha_composite(background, image_rgba) alpha_composite = alpha_composite.convert("RGB") return alpha_composite class IdeficsImageProcessor(BaseImageProcessor): r""" Constructs a Idefics image processor. Args: image_size (`int`, *optional*, defaults to 224): Resize to image size image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. image_num_channels (`int`, *optional*, defaults to 3): Number of image channels. """ model_input_names = ["pixel_values"] def __init__( self, image_size: int = 224, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, image_num_channels: Optional[int] = 3, **kwargs, ) -> None: super().__init__(**kwargs) self.image_size = image_size self.image_num_channels = image_num_channels self.image_mean = image_mean self.image_std = image_std def preprocess( self, images: ImageInput, image_num_channels: Optional[int] = 3, image_size: Optional[Dict[str, int]] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, transform: Callable = None, **kwargs, ) -> TensorType.PYTORCH: """ Preprocess a batch of images. Args: images (`ImageInput`): A list of images to preprocess. image_size (`int`, *optional*, defaults to `self.image_size`): Resize to image size image_num_channels (`int`, *optional*, defaults to `self.image_num_channels`): Number of image channels. image_mean (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IDEFICS_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. Can be overridden by the `image_std` parameter in the `preprocess` method. transform (`Callable`, *optional*, defaults to `None`): A custom transform function that accepts a single image can be passed for training. For example, `torchvision.Compose` can be used to compose multiple transforms. If `None` - an inference mode is assumed - and then a preset of inference-specific transforms will be applied to the images Returns: a PyTorch tensor of the processed images """ image_size = image_size if image_size is not None else self.image_size image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = (image_size, image_size) if isinstance(images, list) and len(images) == 0: return [] 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." ) # For training a user needs to pass their own set of transforms as a Callable. # For reference this is what was used in the original IDEFICS training: # transform = transforms.Compose([ # convert_to_rgb, # transforms.RandomResizedCrop((size, size), scale=(0.9, 1.0), interpolation=transforms.InterpolationMode.BICUBIC), # transforms.ToTensor(), # transforms.Normalize(mean=image_mean, std=image_std), # ]) if transform is not None: if not is_torch_available(): raise ImportError("To pass in `transform` torch must be installed") import torch images = [transform(x) for x in images] return torch.stack(images) # for inference we do the exact transforms that were used to train IDEFICS images = [convert_to_rgb(x) for x in images] # further transforms expect numpy arrays images = [to_numpy_array(x) for x in images] images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images] images = [self.rescale(image=image, scale=1 / 255) for image in images] images = [self.normalize(x, mean=image_mean, std=image_std) for x in images] images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images] # TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"] return images

transformers/src/transformers/models/idefics/image_processing_idefics.py/0

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# coding=utf-8 # Copyright 2023 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. """ Convert InstructBLIP checkpoints from the original repository. URL: https://github.com/salesforce/LAVIS/tree/main/projects/instructblip """ import argparse import requests import torch # pip3 install salesforce-lavis # I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch) # also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml # same for Vicuna-13b from lavis.models import load_model_and_preprocess from PIL import Image from transformers import ( AutoTokenizer, BlipImageProcessor, InstructBlipConfig, InstructBlipForConditionalGeneration, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig, LlamaConfig, LlamaTokenizerFast, T5Config, T5TokenizerFast, ) from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD def load_demo_image(): url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") return image # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config): rename_keys = [] # fmt: off # vision encoder rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")) rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")) rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")) rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")) rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")) rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")) for i in range(config.vision_config.num_hidden_layers): rename_keys.append((f"visual_encoder.blocks.{i}.norm1.weight", f"vision_model.encoder.layers.{i}.layer_norm1.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.norm1.bias", f"vision_model.encoder.layers.{i}.layer_norm1.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.norm2.weight", f"vision_model.encoder.layers.{i}.layer_norm2.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.norm2.bias", f"vision_model.encoder.layers.{i}.layer_norm2.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.attn.qkv.weight", f"vision_model.encoder.layers.{i}.self_attn.qkv.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.weight", f"vision_model.encoder.layers.{i}.self_attn.projection.weight",)) rename_keys.append((f"visual_encoder.blocks.{i}.attn.proj.bias", f"vision_model.encoder.layers.{i}.self_attn.projection.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.weight", f"vision_model.encoder.layers.{i}.mlp.fc1.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc1.bias", f"vision_model.encoder.layers.{i}.mlp.fc1.bias")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.weight", f"vision_model.encoder.layers.{i}.mlp.fc2.weight")) rename_keys.append((f"visual_encoder.blocks.{i}.mlp.fc2.bias", f"vision_model.encoder.layers.{i}.mlp.fc2.bias")) # QFormer rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight")) rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")) # fmt: on return rename_keys def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val def read_in_q_v_bias(state_dict, config): for i in range(config.vision_config.num_hidden_layers): # read in original q and v biases q_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias") v_bias = state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias") # next, set bias in the state dict qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias)) state_dict[f"vision_model.encoder.layers.{i}.self_attn.qkv.bias"] = qkv_bias def get_blip2_config(model_name): image_size = 364 if "coco" in model_name else 224 vision_config = InstructBlipVisionConfig(image_size=image_size).to_dict() # make sure the models have proper bos_token_id and eos_token_id set (important for generation) # seems like flan-T5 models don't have bos_token_id properly set? if "t5-xl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xl", dense_act_fn="gelu", bos_token_id=1).to_dict() elif "t5-xxl" in model_name: text_config = T5Config.from_pretrained("google/flan-t5-xxl", dense_act_fn="gelu", bos_token_id=1).to_dict() elif "vicuna-7b" in model_name: text_config = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf", vocab_size=32001).to_dict() elif "vicuna-13b" in model_name: text_config = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf", vocab_size=32001).to_dict() else: raise ValueError("Model name not supported") # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1 qformer_config = InstructBlipQFormerConfig(vocab_size=30523).to_dict() config = InstructBlipConfig(vision_config=vision_config, text_config=text_config, qformer_config=qformer_config) return config, image_size @torch.no_grad() def convert_blip2_checkpoint(model_name, pytorch_dump_folder_path=None, push_to_hub=False): """ Copy/paste/tweak model's weights to Transformers design. """ qformer_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", truncation_side="left") qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}) if "t5" in model_name: tokenizer = T5TokenizerFast.from_pretrained("google/flan-t5-xl", truncation_side="left") elif "vicuna" in model_name: # the following was used in the original implementation: # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left") # tokenizer.add_special_tokens({"pad_token": "[PAD]"}) # tokenizer.add_special_tokens({"bos_token": "</s>"}) # tokenizer.add_special_tokens({"eos_token": "</s>"}) # tokenizer.add_special_tokens({"unk_token": "</s>"}) tokenizer = LlamaTokenizerFast.from_pretrained( "huggyllama/llama-7b", truncation_side="left", bos_token="</s>", unk_token="</s>" ) tokenizer.add_special_tokens({"pad_token": "[PAD]"}) config, image_size = get_blip2_config(model_name) hf_model = InstructBlipForConditionalGeneration(config).eval() model_name_to_original = { "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"), "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"), "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"), "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"), } name, type = model_name_to_original[model_name] # load original model print("Loading original model...") hf_model_device = "cuda:1" if torch.cuda.is_available() else "cpu" lavis_device = "cuda:2" if torch.cuda.is_available() else "cpu" original_model, vis_processors, _ = load_model_and_preprocess( name=name, model_type=type, is_eval=True, device=lavis_device ) original_model.eval() print("Done!") # update state dict keys state_dict = original_model.state_dict() rename_keys = create_rename_keys(config) for src, dest in rename_keys: rename_key(state_dict, src, dest) # some keys can be renamed efficiently for key, val in state_dict.copy().items(): val = state_dict.pop(key) if key.startswith("Qformer.bert"): key = key.replace("Qformer.bert", "qformer") if "attention.self" in key: key = key.replace("self", "attention") if "llm_proj" in key: key = key.replace("llm_proj", "language_projection") if "t5_proj" in key: key = key.replace("t5_proj", "language_projection") if key.startswith("llm_model"): key = key.replace("llm_model", "language_model") if key.startswith("t5"): key = key.replace("t5", "language") state_dict[key] = val # read in qv biases read_in_q_v_bias(state_dict, config) # note: weights get loaded in torch.float32 by default hf_model.load_state_dict(state_dict, strict=True) image = load_demo_image() prompt = "What is unusual about this image?" # create processor image_processor = BlipImageProcessor( size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD ) processor = InstructBlipProcessor( image_processor=image_processor, tokenizer=tokenizer, qformer_tokenizer=qformer_tokenizer, ) inputs = processor(images=image, text=prompt, return_tensors="pt").to(hf_model_device) # make sure processor creates exact same pixel values original_pixel_values = vis_processors["eval"](image).unsqueeze(0).to(lavis_device) pixel_values = inputs.pixel_values assert torch.allclose(original_pixel_values.to(pixel_values.device), pixel_values) original_model.to(lavis_device) hf_model.to(hf_model_device) with torch.no_grad(): if "vicuna" in model_name: original_logits = original_model({"image": original_pixel_values, "text_input": [prompt]}).logits logits = hf_model(**inputs).logits else: original_logits = original_model( {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]} ).logits label_input_ids = tokenizer("\n", return_tensors="pt").input_ids.to(hf_model_device) labels = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id, -100) logits = hf_model(**inputs, labels=labels).logits print("First values of original logits:", original_logits[0, :3, :3]) print("First values of HF logits:", logits[0, :3, :3]) # assert values assert original_logits.shape == logits.shape atol = 1e-4 if "vicuna" in model_name else 1e-5 assert torch.allclose(original_logits.to(logits.device), logits, atol=atol) print("Looks ok!") print("Generating with original model...") original_outputs = original_model.generate({"image": original_pixel_values, "prompt": prompt}, num_beams=5) # important: we need to cast the weights of the HF model to the appropriate type print("Generating with HF model...") outputs = hf_model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, top_p=0.9, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) if "vicuna" in model_name: # convert output id 0 to 2 (eos_token_id) # TODO add this in the generate method? outputs[outputs == 0] = 2 print("Original generation:", original_outputs) output_text = processor.batch_decode(outputs, skip_special_tokens=True) output_text = [text.strip() for text in output_text] print("HF generation:", output_text) if pytorch_dump_folder_path is not None: processor.save_pretrained(pytorch_dump_folder_path) hf_model.save_pretrained(pytorch_dump_folder_path) if push_to_hub: processor.push_to_hub(f"Salesforce/{model_name}") hf_model.push_to_hub(f"Salesforce/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() choices = [ "instructblip-vicuna-7b", "instructblip-vicuna-13b", "instructblip-flan-t5-xl", "instructblip-flan-t5-xxl", ] parser.add_argument( "--model_name", default="instructblip-flan-t5-xl", choices=choices, type=str, help="Path to hf config.json of model to convert", ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model and processor to the hub after converting", ) args = parser.parse_args() convert_blip2_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

transformers/src/transformers/models/instructblip/convert_instructblip_original_to_pytorch.py/0

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# coding=utf-8 # Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ TF 2.0 LayoutLM model.""" from __future__ import annotations import math import warnings from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutputWithPastAndCrossAttentions, TFBaseModelOutputWithPoolingAndCrossAttentions, TFMaskedLMOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFMaskedLanguageModelingLoss, TFModelInputType, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_layoutlm import LayoutLMConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "LayoutLMConfig" TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/layoutlm-base-uncased", "microsoft/layoutlm-large-uncased", ] class TFLayoutLMEmbeddings(keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.max_position_embeddings = config.max_position_embeddings self.max_2d_position_embeddings = config.max_2d_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape=None): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.config.vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.config.type_vocab_size, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("x_position_embeddings"): self.x_position_embeddings = self.add_weight( name="embeddings", shape=[self.max_2d_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("y_position_embeddings"): self.y_position_embeddings = self.add_weight( name="embeddings", shape=[self.max_2d_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("h_position_embeddings"): self.h_position_embeddings = self.add_weight( name="embeddings", shape=[self.max_2d_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("w_position_embeddings"): self.w_position_embeddings = self.add_weight( name="embeddings", shape=[self.max_2d_position_embeddings, self.hidden_size], initializer=get_initializer(self.initializer_range), ) if self.built: return self.built = True if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) def call( self, input_ids: tf.Tensor = None, bbox: tf.Tensor = None, position_ids: tf.Tensor = None, token_type_ids: tf.Tensor = None, inputs_embeds: tf.Tensor = None, training: bool = False, ) -> tf.Tensor: """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ assert not (input_ids is None and inputs_embeds is None) if input_ids is not None: check_embeddings_within_bounds(input_ids, self.config.vocab_size) inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) if position_ids is None: position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0) if bbox is None: bbox = bbox = tf.fill(input_shape + [4], value=0) try: left_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 0]) upper_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 1]) right_position_embeddings = tf.gather(self.x_position_embeddings, bbox[:, :, 2]) lower_position_embeddings = tf.gather(self.y_position_embeddings, bbox[:, :, 3]) except IndexError as e: raise IndexError("The `bbox`coordinate values should be within 0-1000 range.") from e h_position_embeddings = tf.gather(self.h_position_embeddings, bbox[:, :, 3] - bbox[:, :, 1]) w_position_embeddings = tf.gather(self.w_position_embeddings, bbox[:, :, 2] - bbox[:, :, 0]) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = ( inputs_embeds + position_embeds + token_type_embeds + left_position_embeddings + upper_position_embeddings + right_position_embeddings + lower_position_embeddings + h_position_embeddings + w_position_embeddings ) final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->LayoutLM class TFLayoutLMSelfAttention(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) attention_mask = encoder_attention_mask elif past_key_value is not None: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) key_layer = tf.concat([past_key_value[0], key_layer], axis=2) value_layer = tf.concat([past_key_value[1], value_layer], axis=2) else: key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) if self.is_decoder: # if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) attention_scores = tf.divide(attention_scores, dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFLayoutLMModel call() function) attention_scores = tf.add(attention_scores, attention_mask) # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM class TFLayoutLMSelfOutput(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM class TFLayoutLMAttention(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.self_attention = TFLayoutLMSelfAttention(config, name="self") self.dense_output = TFLayoutLMSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor, encoder_attention_mask: tf.Tensor, past_key_value: Tuple[tf.Tensor], output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.self_attention( hidden_states=input_tensor, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) # add attentions (possibly with past_key_value) if we output them outputs = (attention_output,) + self_outputs[1:] return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM class TFLayoutLMIntermediate(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM class TFLayoutLMOutput(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM class TFLayoutLMLayer(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.attention = TFLayoutLMAttention(config, name="attention") self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = TFLayoutLMAttention(config, name="crossattention") self.intermediate = TFLayoutLMIntermediate(config, name="intermediate") self.bert_output = TFLayoutLMOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_value: Tuple[tf.Tensor] | None, output_attentions: bool, training: bool = False, ) -> Tuple[tf.Tensor]: # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( input_tensor=hidden_states, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=self_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" " by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( input_tensor=attention_output, attention_mask=attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=cross_attn_past_key_value, output_attentions=output_attentions, training=training, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value intermediate_output = self.intermediate(hidden_states=attention_output) layer_output = self.bert_output( hidden_states=intermediate_output, input_tensor=attention_output, training=training ) outputs = (layer_output,) + outputs # add attentions if we output them # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) if getattr(self, "crossattention", None) is not None: with tf.name_scope(self.crossattention.name): self.crossattention.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM class TFLayoutLMEncoder(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFLayoutLMLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, head_mask: tf.Tensor, encoder_hidden_states: tf.Tensor | None, encoder_attention_mask: tf.Tensor | None, past_key_values: Tuple[Tuple[tf.Tensor]] | None, use_cache: Optional[bool], output_attentions: bool, output_hidden_states: bool, return_dict: bool, training: bool = False, ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) past_key_value = past_key_values[i] if past_key_values is not None else None layer_outputs = layer_module( hidden_states=hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, training=training, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if self.config.add_cross_attention and encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None ) return TFBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_attentions, cross_attentions=all_cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM class TFLayoutLMPooler(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), activation="tanh", name="dense", ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(inputs=first_token_tensor) return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM class TFLayoutLMPredictionHeadTransform(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(inputs=hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM class TFLayoutLMLMPredictionHead(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.config = config self.hidden_size = config.hidden_size self.transform = TFLayoutLMPredictionHeadTransform(config, name="transform") # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape=None): self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") if self.built: return self.built = True if getattr(self, "transform", None) is not None: with tf.name_scope(self.transform.name): self.transform.build(None) def get_output_embeddings(self) -> keras.layers.Layer: return self.input_embeddings def set_output_embeddings(self, value: tf.Variable): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self) -> Dict[str, tf.Variable]: return {"bias": self.bias} def set_bias(self, value: tf.Variable): self.bias = value["bias"] self.config.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.transform(hidden_states=hidden_states) seq_length = shape_list(hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states # Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->LayoutLM class TFLayoutLMMLMHead(keras.layers.Layer): def __init__(self, config: LayoutLMConfig, input_embeddings: keras.layers.Layer, **kwargs): super().__init__(**kwargs) self.predictions = TFLayoutLMLMPredictionHead(config, input_embeddings, name="predictions") def call(self, sequence_output: tf.Tensor) -> tf.Tensor: prediction_scores = self.predictions(hidden_states=sequence_output) return prediction_scores def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "predictions", None) is not None: with tf.name_scope(self.predictions.name): self.predictions.build(None) @keras_serializable class TFLayoutLMMainLayer(keras.layers.Layer): config_class = LayoutLMConfig def __init__(self, config: LayoutLMConfig, add_pooling_layer: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.embeddings = TFLayoutLMEmbeddings(config, name="embeddings") self.encoder = TFLayoutLMEncoder(config, name="encoder") self.pooler = TFLayoutLMPooler(config, name="pooler") if add_pooling_layer else None def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.weight = value self.embeddings.vocab_size = shape_list(value)[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = shape_list(input_ids) elif inputs_embeds is not None: input_shape = shape_list(inputs_embeds)[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if attention_mask is None: attention_mask = tf.fill(dims=input_shape, value=1) if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if bbox is None: bbox = tf.fill(dims=input_shape + [4], value=0) embedding_output = self.embeddings( input_ids=input_ids, bbox=bbox, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, training=training, ) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype) one_cst = tf.constant(1.0, dtype=embedding_output.dtype) ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers encoder_outputs = self.encoder( hidden_states=embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, # Need to pass these required positional arguments to `Encoder` encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=None, past_key_values=None, use_cache=False, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None if not return_dict: return ( sequence_output, pooled_output, ) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) class TFLayoutLMPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LayoutLMConfig base_model_prefix = "layoutlm" @property def input_signature(self): signature = super().input_signature signature["bbox"] = tf.TensorSpec(shape=(None, None, 4), dtype=tf.int32, name="bbox") return signature LAYOUTLM_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Args: config ([`LayoutLMConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ LAYOUTLM_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) bbox (`Numpy array` or `tf.Tensor` of shape `({0}, 4)`, *optional*): Bounding Boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings- 1]`. attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *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) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *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) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare LayoutLM Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLM_START_DOCSTRING, ) class TFLayoutLMModel(TFLayoutLMPreTrainedModel): def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm") @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings( output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC ) def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, encoder_hidden_states: np.ndarray | tf.Tensor | None = None, encoder_attention_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = False, ) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, TFLayoutLMModel >>> import tensorflow as tf >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") >>> model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased") >>> words = ["Hello", "world"] >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] >>> token_boxes = [] >>> for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens)) >>> # add bounding boxes of cls + sep tokens >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] >>> encoding = tokenizer(" ".join(words), return_tensors="tf") >>> input_ids = encoding["input_ids"] >>> attention_mask = encoding["attention_mask"] >>> token_type_ids = encoding["token_type_ids"] >>> bbox = tf.convert_to_tensor([token_boxes]) >>> outputs = model( ... input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids ... ) >>> last_hidden_states = outputs.last_hidden_state ```""" outputs = self.layoutlm( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlm", None) is not None: with tf.name_scope(self.layoutlm.name): self.layoutlm.build(None) @add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING) class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [ r"pooler", r"cls.seq_relationship", r"cls.predictions.decoder.weight", r"nsp___cls", ] def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) if config.is_decoder: logger.warning( "If you want to use `TFLayoutLMForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") self.mlm = TFLayoutLMMLMHead(config, input_embeddings=self.layoutlm.embeddings, name="mlm___cls") def get_lm_head(self) -> keras.layers.Layer: return self.mlm.predictions def get_prefix_bias_name(self) -> str: warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning) return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` Returns: Examples: ```python >>> from transformers import AutoTokenizer, TFLayoutLMForMaskedLM >>> import tensorflow as tf >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") >>> model = TFLayoutLMForMaskedLM.from_pretrained("microsoft/layoutlm-base-uncased") >>> words = ["Hello", "[MASK]"] >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] >>> token_boxes = [] >>> for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens)) >>> # add bounding boxes of cls + sep tokens >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] >>> encoding = tokenizer(" ".join(words), return_tensors="tf") >>> input_ids = encoding["input_ids"] >>> attention_mask = encoding["attention_mask"] >>> token_type_ids = encoding["token_type_ids"] >>> bbox = tf.convert_to_tensor([token_boxes]) >>> labels = tokenizer("Hello world", return_tensors="tf")["input_ids"] >>> outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=labels, ... ) >>> loss = outputs.loss ```""" outputs = self.layoutlm( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] prediction_scores = self.mlm(sequence_output=sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlm", None) is not None: with tf.name_scope(self.layoutlm.name): self.layoutlm.build(None) if getattr(self, "mlm", None) is not None: with tf.name_scope(self.mlm.name): self.mlm.build(None) @add_start_docstrings( """ LayoutLM Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, LAYOUTLM_START_DOCSTRING, ) class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship"] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.classifier = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` 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). Returns: Examples: ```python >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification >>> import tensorflow as tf >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased") >>> words = ["Hello", "world"] >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] >>> token_boxes = [] >>> for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens)) >>> # add bounding boxes of cls + sep tokens >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] >>> encoding = tokenizer(" ".join(words), return_tensors="tf") >>> input_ids = encoding["input_ids"] >>> attention_mask = encoding["attention_mask"] >>> token_type_ids = encoding["token_type_ids"] >>> bbox = tf.convert_to_tensor([token_boxes]) >>> sequence_label = tf.convert_to_tensor([1]) >>> outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=sequence_label, ... ) >>> loss = outputs.loss >>> logits = outputs.logits ```""" outputs = self.layoutlm( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs[1] pooled_output = self.dropout(inputs=pooled_output, training=training) logits = self.classifier(inputs=pooled_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlm", None) is not None: with tf.name_scope(self.layoutlm.name): self.layoutlm.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ LayoutLM Model 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. """, LAYOUTLM_START_DOCSTRING, ) class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [ r"pooler", r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship", ] _keys_to_ignore_on_load_missing = [r"dropout"] def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.classifier = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Examples: ```python >>> import tensorflow as tf >>> from transformers import AutoTokenizer, TFLayoutLMForTokenClassification >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") >>> model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased") >>> words = ["Hello", "world"] >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782] >>> token_boxes = [] >>> for word, box in zip(words, normalized_word_boxes): ... word_tokens = tokenizer.tokenize(word) ... token_boxes.extend([box] * len(word_tokens)) >>> # add bounding boxes of cls + sep tokens >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]] >>> encoding = tokenizer(" ".join(words), return_tensors="tf") >>> input_ids = encoding["input_ids"] >>> attention_mask = encoding["attention_mask"] >>> token_type_ids = encoding["token_type_ids"] >>> bbox = tf.convert_to_tensor([token_boxes]) >>> token_labels = tf.convert_to_tensor([1, 1, 0, 0]) >>> outputs = model( ... input_ids=input_ids, ... bbox=bbox, ... attention_mask=attention_mask, ... token_type_ids=token_type_ids, ... labels=token_labels, ... ) >>> loss = outputs.loss >>> logits = outputs.logits ```""" outputs = self.layoutlm( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] sequence_output = self.dropout(inputs=sequence_output, training=training) logits = self.classifier(inputs=sequence_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlm", None) is not None: with tf.name_scope(self.layoutlm.name): self.layoutlm.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ LayoutLM Model with a span classification head on top for extractive question-answering tasks such as [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the final hidden-states output to compute `span start logits` and `span end logits`). """, LAYOUTLM_START_DOCSTRING, ) class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [ r"pooler", r"mlm___cls", r"nsp___cls", r"cls.predictions", r"cls.seq_relationship", ] def __init__(self, config: LayoutLMConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.layoutlm = TFLayoutLMMainLayer(config, add_pooling_layer=True, name="layoutlm") self.qa_outputs = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: TFModelInputType | None = None, bbox: np.ndarray | tf.Tensor | None = None, attention_mask: np.ndarray | tf.Tensor | None = None, token_type_ids: np.ndarray | tf.Tensor | None = None, position_ids: np.ndarray | tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, inputs_embeds: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: np.ndarray | tf.Tensor | None = None, end_positions: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: r""" start_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: Examples: ```python >>> import tensorflow as tf >>> from transformers import AutoTokenizer, TFLayoutLMForQuestionAnswering >>> from datasets import load_dataset >>> tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-qa", add_prefix_space=True) >>> model = TFLayoutLMForQuestionAnswering.from_pretrained("impira/layoutlm-document-qa", revision="1e3ebac") >>> dataset = load_dataset("nielsr/funsd", split="train") >>> example = dataset[0] >>> question = "what's his name?" >>> words = example["words"] >>> boxes = example["bboxes"] >>> encoding = tokenizer( ... question.split(), words, is_split_into_words=True, return_token_type_ids=True, return_tensors="tf" ... ) >>> bbox = [] >>> for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)): ... if s == 1: ... bbox.append(boxes[w]) ... elif i == tokenizer.sep_token_id: ... bbox.append([1000] * 4) ... else: ... bbox.append([0] * 4) >>> encoding["bbox"] = tf.convert_to_tensor([bbox]) >>> word_ids = encoding.word_ids(0) >>> outputs = model(**encoding) >>> loss = outputs.loss >>> start_scores = outputs.start_logits >>> end_scores = outputs.end_logits >>> start, end = word_ids[tf.math.argmax(start_scores, -1)[0]], word_ids[tf.math.argmax(end_scores, -1)[0]] >>> print(" ".join(words[start : end + 1])) M. Hamann P. Harper, P. Martinez ```""" outputs = self.layoutlm( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(inputs=sequence_output) start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1) start_logits = tf.squeeze(input=start_logits, axis=-1) end_logits = tf.squeeze(input=end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions} labels["end_position"] = end_positions loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlm", None) is not None: with tf.name_scope(self.layoutlm.name): self.layoutlm.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build([None, None, self.config.hidden_size])

transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py/0

{ "file_path": "transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py", "repo_id": "transformers", "token_count": 31687 }

314

# coding=utf-8 # Copyright 2022 Microsoft Research and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """TF 2.0 LayoutLMv3 model.""" from __future__ import annotations import collections import math from typing import List, Optional, Tuple, Union import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFTokenClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import check_embeddings_within_bounds from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings from .configuration_layoutlmv3 import LayoutLMv3Config _CONFIG_FOR_DOC = "LayoutLMv3Config" _DUMMY_INPUT_IDS = [ [7, 6, 1], [1, 2, 0], ] _DUMMY_BBOX = [ [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]], ] TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/layoutlmv3-base", "microsoft/layoutlmv3-large", # See all LayoutLMv3 models at https://huggingface.co/models?filter=layoutlmv3 ] LARGE_NEGATIVE = -1e8 class TFLayoutLMv3PatchEmbeddings(keras.layers.Layer): """LayoutLMv3 image (patch) embeddings.""" def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) patch_sizes = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) self.proj = keras.layers.Conv2D( filters=config.hidden_size, kernel_size=patch_sizes, strides=patch_sizes, padding="valid", data_format="channels_last", use_bias=True, kernel_initializer=get_initializer(config.initializer_range), name="proj", ) self.hidden_size = config.hidden_size self.num_patches = (config.input_size**2) // (patch_sizes[0] * patch_sizes[1]) self.config = config def call(self, pixel_values: tf.Tensor) -> tf.Tensor: # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. pixel_values = tf.transpose(pixel_values, perm=[0, 2, 3, 1]) embeddings = self.proj(pixel_values) embeddings = tf.reshape(embeddings, (-1, self.num_patches, self.hidden_size)) return embeddings def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "proj", None) is not None: with tf.name_scope(self.proj.name): self.proj.build([None, None, None, self.config.num_channels]) class TFLayoutLMv3TextEmbeddings(keras.layers.Layer): """ LayoutLMv3 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings. """ def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.word_embeddings = keras.layers.Embedding( config.vocab_size, config.hidden_size, embeddings_initializer=get_initializer(config.initializer_range), name="word_embeddings", ) self.token_type_embeddings = keras.layers.Embedding( config.type_vocab_size, config.hidden_size, embeddings_initializer=get_initializer(config.initializer_range), name="token_type_embeddings", ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) self.padding_token_index = config.pad_token_id self.position_embeddings = keras.layers.Embedding( config.max_position_embeddings, config.hidden_size, embeddings_initializer=get_initializer(config.initializer_range), name="position_embeddings", ) self.x_position_embeddings = keras.layers.Embedding( config.max_2d_position_embeddings, config.coordinate_size, embeddings_initializer=get_initializer(config.initializer_range), name="x_position_embeddings", ) self.y_position_embeddings = keras.layers.Embedding( config.max_2d_position_embeddings, config.coordinate_size, embeddings_initializer=get_initializer(config.initializer_range), name="y_position_embeddings", ) self.h_position_embeddings = keras.layers.Embedding( config.max_2d_position_embeddings, config.shape_size, embeddings_initializer=get_initializer(config.initializer_range), name="h_position_embeddings", ) self.w_position_embeddings = keras.layers.Embedding( config.max_2d_position_embeddings, config.shape_size, embeddings_initializer=get_initializer(config.initializer_range), name="w_position_embeddings", ) self.max_2d_positions = config.max_2d_position_embeddings self.config = config def calculate_spatial_position_embeddings(self, bbox: tf.Tensor) -> tf.Tensor: try: left_position_ids = bbox[:, :, 0] upper_position_ids = bbox[:, :, 1] right_position_ids = bbox[:, :, 2] lower_position_ids = bbox[:, :, 3] except IndexError as exception: raise IndexError("Bounding box is not of shape (batch_size, seq_length, 4).") from exception try: left_position_embeddings = self.x_position_embeddings(left_position_ids) upper_position_embeddings = self.y_position_embeddings(upper_position_ids) right_position_embeddings = self.x_position_embeddings(right_position_ids) lower_position_embeddings = self.y_position_embeddings(lower_position_ids) except IndexError as exception: raise IndexError( f"The `bbox` coordinate values should be within 0-{self.max_2d_positions} range." ) from exception max_position_id = self.max_2d_positions - 1 h_position_embeddings = self.h_position_embeddings( tf.clip_by_value(bbox[:, :, 3] - bbox[:, :, 1], 0, max_position_id) ) w_position_embeddings = self.w_position_embeddings( tf.clip_by_value(bbox[:, :, 2] - bbox[:, :, 0], 0, max_position_id) ) # LayoutLMv1 sums the spatial embeddings, but LayoutLMv3 concatenates them. spatial_position_embeddings = tf.concat( [ left_position_embeddings, upper_position_embeddings, right_position_embeddings, lower_position_embeddings, h_position_embeddings, w_position_embeddings, ], axis=-1, ) return spatial_position_embeddings def create_position_ids_from_inputs_embeds(self, inputs_embds: tf.Tensor) -> tf.Tensor: """ We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position ids. """ input_shape = tf.shape(inputs_embds) sequence_length = input_shape[1] start_index = self.padding_token_index + 1 end_index = self.padding_token_index + sequence_length + 1 position_ids = tf.range(start_index, end_index, dtype=tf.int32) batch_size = input_shape[0] position_ids = tf.reshape(position_ids, (1, sequence_length)) position_ids = tf.tile(position_ids, (batch_size, 1)) return position_ids def create_position_ids_from_input_ids(self, input_ids: tf.Tensor) -> tf.Tensor: """ Replace non-padding symbols with their position numbers. Position numbers begin at padding_token_index + 1. """ mask = tf.cast(tf.not_equal(input_ids, self.padding_token_index), input_ids.dtype) position_ids = tf.cumsum(mask, axis=1) * mask position_ids = position_ids + self.padding_token_index return position_ids def create_position_ids(self, input_ids: tf.Tensor, inputs_embeds: tf.Tensor) -> tf.Tensor: if input_ids is None: return self.create_position_ids_from_inputs_embeds(inputs_embeds) else: return self.create_position_ids_from_input_ids(input_ids) def call( self, input_ids: tf.Tensor | None = None, bbox: tf.Tensor = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, training: bool = False, ) -> tf.Tensor: if position_ids is None: position_ids = self.create_position_ids(input_ids, inputs_embeds) if input_ids is not None: input_shape = tf.shape(input_ids) else: input_shape = tf.shape(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.zeros(input_shape, dtype=position_ids.dtype) if inputs_embeds is None: check_embeddings_within_bounds(input_ids, self.word_embeddings.input_dim) inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox) embeddings += spatial_position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings, training=training) return embeddings def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "word_embeddings", None) is not None: with tf.name_scope(self.word_embeddings.name): self.word_embeddings.build(None) if getattr(self, "token_type_embeddings", None) is not None: with tf.name_scope(self.token_type_embeddings.name): self.token_type_embeddings.build(None) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) if getattr(self, "position_embeddings", None) is not None: with tf.name_scope(self.position_embeddings.name): self.position_embeddings.build(None) if getattr(self, "x_position_embeddings", None) is not None: with tf.name_scope(self.x_position_embeddings.name): self.x_position_embeddings.build(None) if getattr(self, "y_position_embeddings", None) is not None: with tf.name_scope(self.y_position_embeddings.name): self.y_position_embeddings.build(None) if getattr(self, "h_position_embeddings", None) is not None: with tf.name_scope(self.h_position_embeddings.name): self.h_position_embeddings.build(None) if getattr(self, "w_position_embeddings", None) is not None: with tf.name_scope(self.w_position_embeddings.name): self.w_position_embeddings.build(None) class TFLayoutLMv3SelfAttention(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.attention_score_normaliser = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query", ) self.key = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key", ) self.value = keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value", ) self.dropout = keras.layers.Dropout(config.attention_probs_dropout_prob) self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias self.config = config def transpose_for_scores(self, x: tf.Tensor): shape = tf.shape(x) new_shape = ( shape[0], # batch_size shape[1], # seq_length self.num_attention_heads, self.attention_head_size, ) x = tf.reshape(x, new_shape) return tf.transpose(x, perm=[0, 2, 1, 3]) # batch_size, num_heads, seq_length, attention_head_size def cogview_attention(self, attention_scores: tf.Tensor, alpha: Union[float, int] = 32): """ https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation (PB-Relax). A replacement of the original keras.layers.Softmax(axis=-1)(attention_scores). Seems the new attention_probs will result in a slower speed and a little bias. Can use tf.debugging.assert_near(standard_attention_probs, cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better. """ scaled_attention_scores = attention_scores / alpha max_value = tf.expand_dims(tf.reduce_max(scaled_attention_scores, axis=-1), axis=-1) new_attention_scores = (scaled_attention_scores - max_value) * alpha return tf.math.softmax(new_attention_scores, axis=-1) def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None, head_mask: tf.Tensor | None, output_attentions: bool, rel_pos: tf.Tensor | None = None, rel_2d_pos: tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor]]: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(self.query(hidden_states)) # Take the dot product between "query" and "key" to get the raw attention scores. normalised_query_layer = query_layer / self.attention_score_normaliser transposed_key_layer = tf.transpose( key_layer, perm=[0, 1, 3, 2] ) # batch_size, num_heads, attention_head_size, seq_length attention_scores = tf.matmul(normalised_query_layer, transposed_key_layer) if self.has_relative_attention_bias and self.has_spatial_attention_bias: attention_scores += (rel_pos + rel_2d_pos) / self.attention_score_normaliser elif self.has_relative_attention_bias: attention_scores += rel_pos / self.attention_score_normaliser if attention_mask is not None: # Apply the attention mask (is precomputed for all layers in TFLayoutLMv3Model call() function) attention_scores += attention_mask # Normalize the attention scores to probabilities. # Use the trick of CogView paper to stabilize training. attention_probs = self.cogview_attention(attention_scores) attention_probs = self.dropout(attention_probs, training=training) # Mask heads if we want to. if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose( context_layer, perm=[0, 2, 1, 3] ) # batch_size, seq_length, num_heads, attention_head_size shape = tf.shape(context_layer) context_layer = tf.reshape( context_layer, (shape[0], shape[1], self.all_head_size) ) # batch_size, seq_length, num_heads * attention_head_size outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) # Copied from models.roberta.modeling_tf_roberta.TFRobertaSelfOutput class TFLayoutLMv3SelfOutput(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) class TFLayoutLMv3Attention(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.self_attention = TFLayoutLMv3SelfAttention(config, name="self") self.self_output = TFLayoutLMv3SelfOutput(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None, head_mask: tf.Tensor | None, output_attentions: bool, rel_pos: tf.Tensor | None = None, rel_2d_pos: tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor]]: self_outputs = self.self_attention( hidden_states, attention_mask, head_mask, output_attentions, rel_pos, rel_2d_pos, training=training, ) attention_output = self.self_output(self_outputs[0], hidden_states, training=training) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "self_attention", None) is not None: with tf.name_scope(self.self_attention.name): self.self_attention.build(None) if getattr(self, "self_output", None) is not None: with tf.name_scope(self.self_output.name): self.self_output.build(None) # Copied from models.roberta.modeling_tf_bert.TFRobertaIntermediate class TFLayoutLMv3Intermediate(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) # Copied from models.roberta.modeling_tf_bert.TFRobertaOutput class TFLayoutLMv3Output(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) class TFLayoutLMv3Layer(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.attention = TFLayoutLMv3Attention(config, name="attention") self.intermediate = TFLayoutLMv3Intermediate(config, name="intermediate") self.bert_output = TFLayoutLMv3Output(config, name="output") def call( self, hidden_states: tf.Tensor, attention_mask: tf.Tensor | None, head_mask: tf.Tensor | None, output_attentions: bool, rel_pos: tf.Tensor | None = None, rel_2d_pos: tf.Tensor | None = None, training: bool = False, ) -> Union[Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor]]: self_attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions=output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, training=training, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights intermediate_output = self.intermediate(attention_output) layer_output = self.bert_output(intermediate_output, attention_output, training=training) outputs = (layer_output,) + outputs return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "bert_output", None) is not None: with tf.name_scope(self.bert_output.name): self.bert_output.build(None) class TFLayoutLMv3Encoder(keras.layers.Layer): def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.config = config self.layer = [TFLayoutLMv3Layer(config, name=f"layer.{i}") for i in range(config.num_hidden_layers)] self.has_relative_attention_bias = config.has_relative_attention_bias self.has_spatial_attention_bias = config.has_spatial_attention_bias if self.has_relative_attention_bias: self.rel_pos_bins = config.rel_pos_bins self.max_rel_pos = config.max_rel_pos self.rel_pos_bias = keras.layers.Dense( units=config.num_attention_heads, kernel_initializer=get_initializer(config.initializer_range), use_bias=False, name="rel_pos_bias", ) if self.has_spatial_attention_bias: self.max_rel_2d_pos = config.max_rel_2d_pos self.rel_2d_pos_bins = config.rel_2d_pos_bins self.rel_pos_x_bias = keras.layers.Dense( units=config.num_attention_heads, kernel_initializer=get_initializer(config.initializer_range), use_bias=False, name="rel_pos_x_bias", ) self.rel_pos_y_bias = keras.layers.Dense( units=config.num_attention_heads, kernel_initializer=get_initializer(config.initializer_range), use_bias=False, name="rel_pos_y_bias", ) def relative_position_bucket(self, relative_positions: tf.Tensor, num_buckets: int, max_distance: int): # the negative relative positions are assigned to the interval [0, num_buckets / 2] # we deal with this by assigning absolute relative positions to the interval [0, num_buckets / 2] # and then offsetting the positive relative positions by num_buckets / 2 at the end num_buckets = num_buckets // 2 buckets = tf.abs(relative_positions) # half of the buckets are for exact increments in positions max_exact_buckets = num_buckets // 2 is_small = buckets < max_exact_buckets # the other half of the buckets are for logarithmically bigger bins in positions up to max_distance buckets_log_ratio = tf.math.log(tf.cast(buckets, tf.float32) / max_exact_buckets) distance_log_ratio = math.log(max_distance / max_exact_buckets) buckets_big_offset = ( buckets_log_ratio / distance_log_ratio * (num_buckets - max_exact_buckets) ) # scale is [0, num_buckets - max_exact_buckets] buckets_big = max_exact_buckets + buckets_big_offset # scale is [max_exact_buckets, num_buckets] buckets_big = tf.cast(buckets_big, buckets.dtype) buckets_big = tf.minimum(buckets_big, num_buckets - 1) return (tf.cast(relative_positions > 0, buckets.dtype) * num_buckets) + tf.where( is_small, buckets, buckets_big ) def _cal_pos_emb( self, dense_layer: keras.layers.Dense, position_ids: tf.Tensor, num_buckets: int, max_distance: int, ): rel_pos_matrix = tf.expand_dims(position_ids, axis=-2) - tf.expand_dims(position_ids, axis=-1) rel_pos = self.relative_position_bucket(rel_pos_matrix, num_buckets, max_distance) rel_pos_one_hot = tf.one_hot(rel_pos, depth=num_buckets, dtype=self.compute_dtype) embedding = dense_layer(rel_pos_one_hot) # batch_size, seq_length, seq_length, num_heads --> batch_size, num_heads, seq_length, seq_length embedding = tf.transpose(embedding, [0, 3, 1, 2]) embedding = tf.cast(embedding, dtype=self.compute_dtype) return embedding def _cal_1d_pos_emb(self, position_ids: tf.Tensor): return self._cal_pos_emb(self.rel_pos_bias, position_ids, self.rel_pos_bins, self.max_rel_pos) def _cal_2d_pos_emb(self, bbox: tf.Tensor): position_coord_x = bbox[:, :, 0] # left position_coord_y = bbox[:, :, 3] # bottom rel_pos_x = self._cal_pos_emb( self.rel_pos_x_bias, position_coord_x, self.rel_2d_pos_bins, self.max_rel_2d_pos, ) rel_pos_y = self._cal_pos_emb( self.rel_pos_y_bias, position_coord_y, self.rel_2d_pos_bins, self.max_rel_2d_pos, ) rel_2d_pos = rel_pos_x + rel_pos_y return rel_2d_pos def call( self, hidden_states: tf.Tensor, bbox: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, position_ids: tf.Tensor | None = None, training: bool = False, ) -> Union[ TFBaseModelOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], ]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None layer_outputs = layer_module( hidden_states, attention_mask, layer_head_mask, output_attentions, rel_pos=rel_pos, rel_2d_pos=rel_2d_pos, training=training, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if return_dict: return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) else: return tuple( value for value in [hidden_states, all_hidden_states, all_self_attentions] if value is not None ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "rel_pos_bias", None) is not None: with tf.name_scope(self.rel_pos_bias.name): self.rel_pos_bias.build([None, None, self.rel_pos_bins]) if getattr(self, "rel_pos_x_bias", None) is not None: with tf.name_scope(self.rel_pos_x_bias.name): self.rel_pos_x_bias.build([None, None, self.rel_2d_pos_bins]) if getattr(self, "rel_pos_y_bias", None) is not None: with tf.name_scope(self.rel_pos_y_bias.name): self.rel_pos_y_bias.build([None, None, self.rel_2d_pos_bins]) if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFLayoutLMv3MainLayer(keras.layers.Layer): config_class = LayoutLMv3Config def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.config = config if config.text_embed: self.embeddings = TFLayoutLMv3TextEmbeddings(config, name="embeddings") if config.visual_embed: self.patch_embed = TFLayoutLMv3PatchEmbeddings(config, name="patch_embed") self.LayerNorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout") if config.has_relative_attention_bias or config.has_spatial_attention_bias: image_size = config.input_size // config.patch_size self.init_visual_bbox(image_size=(image_size, image_size)) self.norm = keras.layers.LayerNormalization(epsilon=1e-6, name="norm") self.encoder = TFLayoutLMv3Encoder(config, name="encoder") def build(self, input_shape=None): if self.config.visual_embed: image_size = self.config.input_size // self.config.patch_size self.cls_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer="zeros", trainable=True, dtype=tf.float32, name="cls_token", ) self.pos_embed = self.add_weight( shape=(1, image_size * image_size + 1, self.config.hidden_size), initializer="zeros", trainable=True, dtype=tf.float32, name="pos_embed", ) if self.built: return self.built = True if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "patch_embed", None) is not None: with tf.name_scope(self.patch_embed.name): self.patch_embed.build(None) if getattr(self, "LayerNorm", None) is not None: with tf.name_scope(self.LayerNorm.name): self.LayerNorm.build([None, None, self.config.hidden_size]) if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) if getattr(self, "norm", None) is not None: with tf.name_scope(self.norm.name): self.norm.build([None, None, self.config.hidden_size]) def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings.word_embeddings def set_input_embeddings(self, value: tf.Variable): self.embeddings.word_embeddings.weight = value # Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError def init_visual_bbox(self, image_size: Tuple[int, int], max_len: int = 1000): # We should not hardcode max_len to 1000, but it is done by the reference implementation, # so we keep it for compatibility with the pretrained weights. The more correct approach # would have been to pass on max_len=config.max_2d_position_embeddings - 1. height, width = image_size visual_bbox_x = tf.range(0, max_len * (width + 1), max_len) // width visual_bbox_x = tf.expand_dims(visual_bbox_x, axis=0) visual_bbox_x = tf.tile(visual_bbox_x, [width, 1]) # (width, width + 1) visual_bbox_y = tf.range(0, max_len * (height + 1), max_len) // height visual_bbox_y = tf.expand_dims(visual_bbox_y, axis=1) visual_bbox_y = tf.tile(visual_bbox_y, [1, height]) # (height + 1, height) visual_bbox = tf.stack( [visual_bbox_x[:, :-1], visual_bbox_y[:-1], visual_bbox_x[:, 1:], visual_bbox_y[1:]], axis=-1, ) visual_bbox = tf.reshape(visual_bbox, [-1, 4]) cls_token_box = tf.constant([[1, 1, max_len - 1, max_len - 1]], dtype=tf.int32) self.visual_bbox = tf.concat([cls_token_box, visual_bbox], axis=0) def calculate_visual_bbox(self, batch_size: int, dtype: tf.DType): visual_bbox = tf.expand_dims(self.visual_bbox, axis=0) visual_bbox = tf.tile(visual_bbox, [batch_size, 1, 1]) visual_bbox = tf.cast(visual_bbox, dtype=dtype) return visual_bbox def embed_image(self, pixel_values: tf.Tensor) -> tf.Tensor: embeddings = self.patch_embed(pixel_values) # add [CLS] token batch_size = tf.shape(embeddings)[0] cls_tokens = tf.tile(self.cls_token, [batch_size, 1, 1]) embeddings = tf.concat([cls_tokens, embeddings], axis=1) # add position embeddings if getattr(self, "pos_embed", None) is not None: embeddings += self.pos_embed embeddings = self.norm(embeddings) return embeddings def get_extended_attention_mask(self, attention_mask: tf.Tensor) -> tf.Tensor: # Adapted from transformers.modelling_utils.ModuleUtilsMixin.get_extended_attention_mask n_dims = len(attention_mask.shape) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. if n_dims == 3: extended_attention_mask = tf.expand_dims(attention_mask, axis=1) elif n_dims == 2: # Provided a padding mask of dimensions [batch_size, seq_length]. # Make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]. extended_attention_mask = tf.expand_dims(attention_mask, axis=1) # (batch_size, 1, seq_length) extended_attention_mask = tf.expand_dims(extended_attention_mask, axis=1) # (batch_size, 1, 1, seq_length) else: raise ValueError(f"Wrong shape for attention_mask (shape {attention_mask.shape}).") # Since attention_mask is 1.0 for positions we want to attend and 0.0 for # masked positions, this operation will create a tensor which is 0.0 for # positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, self.compute_dtype) extended_attention_mask = (1.0 - extended_attention_mask) * LARGE_NEGATIVE return extended_attention_mask def get_head_mask(self, head_mask: tf.Tensor | None) -> Union[tf.Tensor, List[tf.Tensor | None]]: if head_mask is None: return [None] * self.config.num_hidden_layers n_dims = tf.rank(head_mask) if n_dims == 1: # Gets a tensor with masks for each head (H). head_mask = tf.expand_dims(head_mask, axis=0) # 1, num_heads head_mask = tf.expand_dims(head_mask, axis=0) # 1, 1, num_heads head_mask = tf.expand_dims(head_mask, axis=-1) # 1, 1, num_heads, 1 head_mask = tf.expand_dims(head_mask, axis=-1) # 1, 1, num_heads, 1, 1 head_mask = tf.tile( head_mask, [self.config.num_hidden_layers, 1, 1, 1, 1] ) # seq_length, 1, num_heads, 1, 1 elif n_dims == 2: # Gets a tensor with masks for each layer (L) and head (H). head_mask = tf.expand_dims(head_mask, axis=1) # seq_length, 1, num_heads head_mask = tf.expand_dims(head_mask, axis=-1) # seq_length, 1, num_heads, 1 head_mask = tf.expand_dims(head_mask, axis=-1) # seq_length, 1, num_heads, 1, 1 elif n_dims != 5: raise ValueError(f"Wrong shape for head_mask (shape {head_mask.shape}).") assert tf.rank(head_mask) == 5, f"Got head_mask rank of {tf.rank(head_mask)}, but require 5." head_mask = tf.cast(head_mask, self.compute_dtype) return head_mask @unpack_inputs def call( self, input_ids: tf.Tensor | None = None, bbox: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[ TFBaseModelOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], ]: # This method can be called with a variety of modalities: # 1. text + layout # 2. text + layout + image # 3. image # The complexity of this method is mostly just due to handling of these different modalities. 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.return_dict if input_ids is not None: input_shape = tf.shape(input_ids) batch_size = input_shape[0] seq_length = input_shape[1] elif inputs_embeds is not None: input_shape = tf.shape(inputs_embeds) batch_size = input_shape[0] seq_length = input_shape[1] elif pixel_values is not None: batch_size = tf.shape(pixel_values)[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds or pixel_values") # Determine which integer dtype to use. if input_ids is not None: int_dtype = input_ids.dtype elif bbox is not None: int_dtype = bbox.dtype elif attention_mask is not None: int_dtype = attention_mask.dtype elif token_type_ids is not None: int_dtype = token_type_ids.dtype else: int_dtype = tf.int32 if input_ids is not None or inputs_embeds is not None: if attention_mask is None: attention_mask = tf.ones((batch_size, seq_length), dtype=int_dtype) if token_type_ids is None: token_type_ids = tf.zeros((batch_size, seq_length), dtype=int_dtype) if bbox is None: bbox = tf.zeros((batch_size, seq_length, 4), dtype=int_dtype) embedding_output = self.embeddings( input_ids=input_ids, bbox=bbox, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, training=training, ) final_bbox = None final_position_ids = None if pixel_values is not None: # embed image visual_embeddings = self.embed_image(pixel_values) # calculate attention mask visual_attention_mask = tf.ones((batch_size, tf.shape(visual_embeddings)[1]), dtype=int_dtype) if attention_mask is None: attention_mask = visual_attention_mask else: attention_mask = tf.concat([attention_mask, visual_attention_mask], axis=1) # calculate bounding boxes if self.config.has_spatial_attention_bias: visual_bbox = self.calculate_visual_bbox(batch_size, int_dtype) if bbox is None: final_bbox = visual_bbox else: final_bbox = tf.concat([bbox, visual_bbox], axis=1) # calculate position IDs if self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: visual_position_ids = tf.range(0, tf.shape(visual_embeddings)[1], dtype=int_dtype) visual_position_ids = tf.expand_dims(visual_position_ids, axis=0) visual_position_ids = tf.tile(visual_position_ids, [batch_size, 1]) if input_ids is not None or inputs_embeds is not None: position_ids = tf.expand_dims(tf.range(0, seq_length, dtype=int_dtype), axis=0) position_ids = tf.tile(position_ids, [batch_size, 1]) final_position_ids = tf.concat([position_ids, visual_position_ids], axis=1) else: final_position_ids = visual_position_ids # calculate embeddings if input_ids is None and inputs_embeds is None: embedding_output = visual_embeddings else: embedding_output = tf.concat([embedding_output, visual_embeddings], axis=1) embedding_output = self.LayerNorm(embedding_output) embedding_output = self.dropout(embedding_output, training=training) elif self.config.has_relative_attention_bias or self.config.has_spatial_attention_bias: if self.config.has_relative_attention_bias: position_ids = tf.expand_dims(tf.range(0, seq_length, dtype=int_dtype), axis=0) position_ids = tf.tile(position_ids, [batch_size, 1]) final_position_ids = position_ids if self.config.has_spatial_attention_bias: final_bbox = bbox extended_attention_mask = self.get_extended_attention_mask(attention_mask) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape batch_size x num_heads x seq_length x seq_length # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask) encoder_outputs = self.encoder( embedding_output, bbox=final_bbox, position_ids=final_position_ids, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return TFBaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) return TFBaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class TFLayoutLMv3PreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = LayoutLMv3Config base_model_prefix = "layoutlmv3" @property def input_signature(self): sig = super().input_signature sig["bbox"] = tf.TensorSpec((None, None, 4), tf.int32, name="bbox") return sig LAYOUTLMV3_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. 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 [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! </Tip> Parameters: config ([`LayoutLMv3Config`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ LAYOUTLMV3_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Batch of document images. Each image is divided into patches of shape `(num_channels, config.patch_size, config.patch_size)` and the total number of patches (=`patch_sequence_length`) equals to `((height / config.patch_size) * (width / config.patch_size))`. attention_mask (`tf.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**. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` 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]`. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are position IDs?](../glossary#position-ids) head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare LayoutLMv3 Model transformer outputting raw hidden-states without any specific head on top.", LAYOUTLMV3_START_DOCSTRING, ) class TFLayoutLMv3Model(TFLayoutLMv3PreTrainedModel): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"position_ids"] def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3") @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: tf.Tensor | None = None, bbox: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[ TFBaseModelOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], ]: r""" Returns: Examples: ```python >>> from transformers import AutoProcessor, TFAutoModel >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = TFAutoModel.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf") >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state ```""" outputs = self.layoutlmv3( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlmv3", None) is not None: with tf.name_scope(self.layoutlmv3.name): self.layoutlmv3.build(None) class TFLayoutLMv3ClassificationHead(keras.layers.Layer): """ Head for sentence-level classification tasks. Reference: RobertaClassificationHead """ def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( config.hidden_size, activation="tanh", kernel_initializer=get_initializer(config.initializer_range), name="dense", ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = keras.layers.Dropout( classifier_dropout, name="dropout", ) self.out_proj = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj", ) self.config = config def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor: outputs = self.dropout(inputs, training=training) outputs = self.dense(outputs) outputs = self.dropout(outputs, training=training) outputs = self.out_proj(outputs) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) if getattr(self, "out_proj", None) is not None: with tf.name_scope(self.out_proj.name): self.out_proj.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset. """, LAYOUTLMV3_START_DOCSTRING, ) class TFLayoutLMv3ForSequenceClassification(TFLayoutLMv3PreTrainedModel, TFSequenceClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"position_ids"] def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(config, **kwargs) self.config = config self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3") self.classifier = TFLayoutLMv3ClassificationHead(config, name="classifier") @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, bbox: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[ TFSequenceClassifierOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor], ]: """ Returns: Examples: ```python >>> from transformers import AutoProcessor, TFAutoModelForSequenceClassification >>> from datasets import load_dataset >>> import tensorflow as tf >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = TFAutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf") >>> sequence_label = tf.convert_to_tensor([1]) >>> outputs = model(**encoding, labels=sequence_label) >>> loss = outputs.loss >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values, training=training, ) sequence_output = outputs[0][:, 0, :] logits = self.classifier(sequence_output, training=training) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlmv3", None) is not None: with tf.name_scope(self.layoutlmv3.name): self.layoutlmv3.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build(None) @add_start_docstrings( """ LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g. for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda). """, LAYOUTLMV3_START_DOCSTRING, ) class TFLayoutLMv3ForTokenClassification(TFLayoutLMv3PreTrainedModel, TFTokenClassificationLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"position_ids"] def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(config, **kwargs) self.num_labels = config.num_labels self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3") self.dropout = keras.layers.Dropout(config.hidden_dropout_prob, name="dropout") if config.num_labels < 10: self.classifier = keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", ) else: self.classifier = TFLayoutLMv3ClassificationHead(config, name="classifier") self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: tf.Tensor | None = None, bbox: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, pixel_values: tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[ TFTokenClassifierOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor], ]: r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Returns: Examples: ```python >>> from transformers import AutoProcessor, TFAutoModelForTokenClassification >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = TFAutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7) >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> word_labels = example["ner_tags"] >>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="tf") >>> outputs = model(**encoding) >>> loss = outputs.loss >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, pixel_values=pixel_values, training=training, ) if input_ids is not None: input_shape = tf.shape(input_ids) else: input_shape = tf.shape(inputs_embeds)[:-1] seq_length = input_shape[1] # only take the text part of the output representations sequence_output = outputs[0][:, :seq_length] sequence_output = self.dropout(sequence_output, training=training) logits = self.classifier(sequence_output) loss = None if labels is None else self.hf_compute_loss(labels, logits) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlmv3", None) is not None: with tf.name_scope(self.layoutlmv3.name): self.layoutlmv3.build(None) if getattr(self, "dropout", None) is not None: with tf.name_scope(self.dropout.name): self.dropout.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) @add_start_docstrings( """ LayoutLMv3 Model with a span classification head on top for extractive question-answering tasks such as [DocVQA](https://rrc.cvc.uab.es/?ch=17) (a linear layer on top of the text part of the hidden-states output to compute `span start logits` and `span end logits`). """, LAYOUTLMV3_START_DOCSTRING, ) class TFLayoutLMv3ForQuestionAnswering(TFLayoutLMv3PreTrainedModel, TFQuestionAnsweringLoss): # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model _keys_to_ignore_on_load_unexpected = [r"position_ids"] def __init__(self, config: LayoutLMv3Config, **kwargs): super().__init__(config, **kwargs) self.num_labels = config.num_labels self.layoutlmv3 = TFLayoutLMv3MainLayer(config, name="layoutlmv3") self.qa_outputs = TFLayoutLMv3ClassificationHead(config, name="qa_outputs") @unpack_inputs @add_start_docstrings_to_model_forward(LAYOUTLMV3_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC) def call( self, input_ids: tf.Tensor | None = None, attention_mask: tf.Tensor | None = None, token_type_ids: tf.Tensor | None = None, position_ids: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, inputs_embeds: tf.Tensor | None = None, start_positions: tf.Tensor | None = None, end_positions: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, bbox: tf.Tensor | None = None, pixel_values: tf.Tensor | None = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[ TFQuestionAnsweringModelOutput, Tuple[tf.Tensor], Tuple[tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor], Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor], ]: r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: Examples: ```python >>> from transformers import AutoProcessor, TFAutoModelForQuestionAnswering >>> from datasets import load_dataset >>> import tensorflow as tf >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = TFAutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> question = "what's his name?" >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="tf") >>> start_positions = tf.convert_to_tensor([1]) >>> end_positions = tf.convert_to_tensor([3]) >>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions) >>> loss = outputs.loss >>> start_scores = outputs.start_logits >>> end_scores = outputs.end_logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.layoutlmv3( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, bbox=bbox, pixel_values=pixel_values, training=training, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output, training=training) start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1) start_logits = tf.squeeze(input=start_logits, axis=-1) end_logits = tf.squeeze(input=end_logits, axis=-1) loss = None if start_positions is not None and end_positions is not None: labels = {"start_position": start_positions, "end_position": end_positions} loss = self.hf_compute_loss(labels, logits=(start_logits, end_logits)) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layoutlmv3", None) is not None: with tf.name_scope(self.layoutlmv3.name): self.layoutlmv3.build(None) if getattr(self, "qa_outputs", None) is not None: with tf.name_scope(self.qa_outputs.name): self.qa_outputs.build(None)

transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py/0

{ "file_path": "transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py", "repo_id": "transformers", "token_count": 34245 }

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# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert LeViT checkpoints from timm.""" import argparse import json from collections import OrderedDict from functools import partial from pathlib import Path import timm import torch from huggingface_hub import hf_hub_download from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger() def convert_weight_and_push( hidden_sizes: int, name: str, config: LevitConfig, save_directory: Path, push_to_hub: bool = True ): print(f"Converting {name}...") with torch.no_grad(): if hidden_sizes == 128: if name[-1] == "S": from_model = timm.create_model("levit_128s", pretrained=True) else: from_model = timm.create_model("levit_128", pretrained=True) if hidden_sizes == 192: from_model = timm.create_model("levit_192", pretrained=True) if hidden_sizes == 256: from_model = timm.create_model("levit_256", pretrained=True) if hidden_sizes == 384: from_model = timm.create_model("levit_384", pretrained=True) from_model.eval() our_model = LevitForImageClassificationWithTeacher(config).eval() huggingface_weights = OrderedDict() weights = from_model.state_dict() og_keys = list(from_model.state_dict().keys()) new_keys = list(our_model.state_dict().keys()) print(len(og_keys), len(new_keys)) for i in range(len(og_keys)): huggingface_weights[new_keys[i]] = weights[og_keys[i]] our_model.load_state_dict(huggingface_weights) x = torch.randn((2, 3, 224, 224)) out1 = from_model(x) out2 = our_model(x).logits assert torch.allclose(out1, out2), "The model logits don't match the original one." checkpoint_name = name print(checkpoint_name) if push_to_hub: our_model.save_pretrained(save_directory / checkpoint_name) image_processor = LevitImageProcessor() image_processor.save_pretrained(save_directory / checkpoint_name) print(f"Pushed {checkpoint_name}") def convert_weights_and_push(save_directory: Path, model_name: str = None, push_to_hub: bool = True): filename = "imagenet-1k-id2label.json" num_labels = 1000 expected_shape = (1, num_labels) repo_id = "huggingface/label-files" num_labels = num_labels id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} id2label = id2label label2id = {v: k for k, v in id2label.items()} ImageNetPreTrainedConfig = partial(LevitConfig, num_labels=num_labels, id2label=id2label, label2id=label2id) names_to_hidden_sizes = { "levit-128S": 128, "levit-128": 128, "levit-192": 192, "levit-256": 256, "levit-384": 384, } names_to_config = { "levit-128S": ImageNetPreTrainedConfig( hidden_sizes=[128, 256, 384], num_attention_heads=[4, 6, 8], depths=[2, 3, 4], key_dim=[16, 16, 16], drop_path_rate=0, ), "levit-128": ImageNetPreTrainedConfig( hidden_sizes=[128, 256, 384], num_attention_heads=[4, 8, 12], depths=[4, 4, 4], key_dim=[16, 16, 16], drop_path_rate=0, ), "levit-192": ImageNetPreTrainedConfig( hidden_sizes=[192, 288, 384], num_attention_heads=[3, 5, 6], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ), "levit-256": ImageNetPreTrainedConfig( hidden_sizes=[256, 384, 512], num_attention_heads=[4, 6, 8], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0, ), "levit-384": ImageNetPreTrainedConfig( hidden_sizes=[384, 512, 768], num_attention_heads=[6, 9, 12], depths=[4, 4, 4], key_dim=[32, 32, 32], drop_path_rate=0.1, ), } if model_name: convert_weight_and_push( names_to_hidden_sizes[model_name], model_name, names_to_config[model_name], save_directory, push_to_hub ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(names_to_hidden_sizes[model_name], model_name, config, save_directory, push_to_hub) return config, expected_shape if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default=None, type=str, help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,", ) parser.add_argument( "--pytorch_dump_folder_path", default="levit-dump-folder/", type=Path, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub") parser.add_argument( "--no-push_to_hub", dest="push_to_hub", action="store_false", help="Do not push model and image processor to the hub", ) args = parser.parse_args() pytorch_dump_folder_path: Path = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

transformers/src/transformers/models/levit/convert_levit_timm_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/levit/convert_levit_timm_to_pytorch.py", "repo_id": "transformers", "token_count": 2739 }

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# Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from huggingface_hub import hf_hub_download from transformers import ( AddedToken, AutoConfig, AutoTokenizer, CLIPImageProcessor, LlavaConfig, LlavaForConditionalGeneration, LlavaProcessor, ) EPILOG_TXT = """Example: python transformers/src/transformers/models/llava/convert_llava_weights_to_hf.py --text_model_id lmsys/vicuna-7b-v1.5 --vision_model_id openai/clip-vit-large-patch14-336 --output_hub_path org/llava-v1.5-7b-conv --old_state_dict_id liuhaotian/llava-v1.5-7b Example for creating the old state dict file with Python: import torch from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM # load model kwargs = {"device_map": "auto", "torch_dtype": torch.float16} model = LlavaLlamaForCausalLM.from_pretrained("liuhaotian/llava-v1.5-7b", low_cpu_mem_usage=True, **kwargs) # load vision tower model.get_vision_tower().load_model() # Save state dict torch.save(model.state_dict(), "tmp/hf_models/llava-v1.5-7b/model_state_dict.bin") """ KEYS_TO_MODIFY_MAPPING = { "model.vision_tower.": "", "model.mm_projector": "multi_modal_projector", "model": "model.model", "vision_model.model": "vision_model", "lm_head": "language_model.lm_head", "model.model": "language_model.model", "multi_modal_projector.0": "multi_modal_projector.linear_1", "multi_modal_projector.2": "multi_modal_projector.linear_2", } def convert_state_dict_to_hf(state_dict): new_state_dict = {} for key, value in state_dict.items(): if key.endswith(".inv_freq"): continue for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items(): if key_to_modify in key: key = key.replace(key_to_modify, new_key) new_state_dict[key] = value return new_state_dict def convert_llava_llama_to_hf(text_model_id, vision_model_id, output_hub_path, old_state_dict_id): torch.set_default_dtype(torch.float16) text_config = AutoConfig.from_pretrained(text_model_id) tokenizer = AutoTokenizer.from_pretrained(text_model_id) tokenizer.add_tokens(AddedToken("<image>", special=True, normalized=False), special_tokens=True) tokenizer.add_special_tokens({"pad_token": "<pad>"}) image_processor = CLIPImageProcessor.from_pretrained(vision_model_id) processor = LlavaProcessor(tokenizer=tokenizer, image_processor=image_processor) config = LlavaConfig(text_config=text_config) config.pad_token_id = 32001 with torch.device("meta"): model = LlavaForConditionalGeneration(config) # Pad to 64 for performance reasons pad_shape = 64 state_dict_path = hf_hub_download(old_state_dict_id, "model_state_dict.bin") state_dict = torch.load(state_dict_path, map_location="cpu") state_dict = convert_state_dict_to_hf(state_dict) model.load_state_dict(state_dict, strict=True, assign=True) pre_expansion_embeddings = model.language_model.model.embed_tokens.weight.data mu = torch.mean(pre_expansion_embeddings, dim=0).float() n = pre_expansion_embeddings.size()[0] sigma = ((pre_expansion_embeddings - mu).T @ (pre_expansion_embeddings - mu)) / n dist = torch.distributions.multivariate_normal.MultivariateNormal(mu, covariance_matrix=1e-5 * sigma) # We add an image token so we resize the model model.resize_token_embeddings(config.text_config.vocab_size + 2, pad_shape) model.language_model.model.embed_tokens.weight.data[32000:] = torch.stack( tuple((dist.sample() for _ in range(model.language_model.model.embed_tokens.weight.data[32000:].shape[0]))), dim=0, ) model.language_model.lm_head.weight.data[32000:] = torch.stack( tuple((dist.sample() for _ in range(model.language_model.lm_head.weight.data[32000:].shape[0]))), dim=0, ) model.push_to_hub(output_hub_path) processor.push_to_hub(output_hub_path) def main(): parser = argparse.ArgumentParser( epilog=EPILOG_TXT, formatter_class=argparse.RawDescriptionHelpFormatter, ) parser.add_argument( "--text_model_id", help="Hub location of the text model", ) parser.add_argument( "--vision_model_id", help="Hub location of the vision model", ) parser.add_argument( "--output_hub_path", help="Location on the hub of the converted model", ) parser.add_argument( "--old_state_dict_id", help="Location on the hub of the raw state dict of the original model. The filename needs to be `model_state_dict.bin`", ) args = parser.parse_args() convert_llava_llama_to_hf(args.text_model_id, args.vision_model_id, args.output_hub_path, args.old_state_dict_id) if __name__ == "__main__": main()

transformers/src/transformers/models/llava/convert_llava_weights_to_hf.py/0

{ "file_path": "transformers/src/transformers/models/llava/convert_llava_weights_to_hf.py", "repo_id": "transformers", "token_count": 2138 }

317

# Copyright 2020 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. import json import os import re import warnings from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "source_spm": "source.spm", "target_spm": "target.spm", "vocab": "vocab.json", "target_vocab_file": "target_vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "source_spm": { "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm" }, "target_spm": { "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm" }, "vocab": { "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json" }, "tokenizer_config_file": { "Helsinki-NLP/opus-mt-en-de": ( "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json" ) }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"Helsinki-NLP/opus-mt-en-de": 512} PRETRAINED_INIT_CONFIGURATION = {} SPIECE_UNDERLINE = "▁" # Example URL https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json class MarianTokenizer(PreTrainedTokenizer): r""" Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: source_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the source language. target_spm (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that contains the vocabulary for the target language. source_lang (`str`, *optional*): A string representing the source language. target_lang (`str`, *optional*): A string representing the target language. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. model_max_length (`int`, *optional*, defaults to 512): The maximum sentence length the model accepts. additional_special_tokens (`List[str]`, *optional*, defaults to `["<eop>", "<eod>"]`): Additional special tokens used by the tokenizer. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Examples: ```python >>> from transformers import MarianForCausalLM, MarianTokenizer >>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de") >>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."] >>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."] # optional >>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True) >>> outputs = model(**inputs) # should work ```""" vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] language_code_re = re.compile(">>.+<<") # type: re.Pattern def __init__( self, source_spm, target_spm, vocab, target_vocab_file=None, source_lang=None, target_lang=None, unk_token="<unk>", eos_token="</s>", pad_token="<pad>", model_max_length=512, sp_model_kwargs: Optional[Dict[str, Any]] = None, separate_vocabs=False, **kwargs, ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs assert Path(source_spm).exists(), f"cannot find spm source {source_spm}" self.separate_vocabs = separate_vocabs self.encoder = load_json(vocab) if str(unk_token) not in self.encoder: raise KeyError("<unk> token must be in the vocab") assert str(pad_token) in self.encoder if separate_vocabs: self.target_encoder = load_json(target_vocab_file) self.decoder = {v: k for k, v in self.target_encoder.items()} self.supported_language_codes = [] else: self.decoder = {v: k for k, v in self.encoder.items()} self.supported_language_codes: list = [k for k in self.encoder if k.startswith(">>") and k.endswith("<<")] self.source_lang = source_lang self.target_lang = target_lang self.spm_files = [source_spm, target_spm] # load SentencePiece model for pre-processing self.spm_source = load_spm(source_spm, self.sp_model_kwargs) self.spm_target = load_spm(target_spm, self.sp_model_kwargs) self.current_spm = self.spm_source self.current_encoder = self.encoder # Multilingual target side: default to using first supported language code. self._setup_normalizer() super().__init__( # bos_token=bos_token, unused. Start decoding with config.decoder_start_token_id source_lang=source_lang, target_lang=target_lang, unk_token=unk_token, eos_token=eos_token, pad_token=pad_token, model_max_length=model_max_length, sp_model_kwargs=self.sp_model_kwargs, target_vocab_file=target_vocab_file, separate_vocabs=separate_vocabs, **kwargs, ) def _setup_normalizer(self): try: from sacremoses import MosesPunctNormalizer self.punc_normalizer = MosesPunctNormalizer(self.source_lang).normalize except (ImportError, FileNotFoundError): warnings.warn("Recommended: pip install sacremoses.") self.punc_normalizer = lambda x: x def normalize(self, x: str) -> str: """Cover moses empty string edge case. They return empty list for '' input!""" return self.punc_normalizer(x) if x else "" def _convert_token_to_id(self, token): return self.current_encoder.get(token, self.current_encoder[self.unk_token]) def remove_language_code(self, text: str): """Remove language codes like >>fr<< before sentencepiece""" match = self.language_code_re.match(text) code: list = [match.group(0)] if match else [] return code, self.language_code_re.sub("", text) def _tokenize(self, text: str) -> List[str]: code, text = self.remove_language_code(text) pieces = self.current_spm.encode(text, out_type=str) return code + pieces def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the decoder.""" return self.decoder.get(index, self.unk_token) def batch_decode(self, sequences, **kwargs): """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]`: The list of decoded sentences. """ return super().batch_decode(sequences, **kwargs) def decode(self, token_ids, **kwargs): """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*): Whether or not to clean up the tokenization spaces. If `None`, will default to `self.clean_up_tokenization_spaces` (available in the `tokenizer_config`). use_source_tokenizer (`bool`, *optional*, defaults to `False`): Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence problems). kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str`: The decoded sentence. """ return super().decode(token_ids, **kwargs) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise""" sp_model = self.spm_source if self._decode_use_source_tokenizer else self.spm_target current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += sp_model.decode_pieces(current_sub_tokens) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += sp_model.decode_pieces(current_sub_tokens) out_string = out_string.replace(SPIECE_UNDERLINE, " ") return out_string.strip() def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: """Build model inputs from a sequence by appending eos_token_id.""" if token_ids_1 is None: return token_ids_0 + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return token_ids_0 + token_ids_1 + [self.eos_token_id] def _switch_to_input_mode(self): self.current_spm = self.spm_source self.current_encoder = self.encoder def _switch_to_target_mode(self): self.current_spm = self.spm_target if self.separate_vocabs: self.current_encoder = self.target_encoder @property def vocab_size(self) -> int: return len(self.encoder) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return saved_files = [] if self.separate_vocabs: out_src_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"], ) out_tgt_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"], ) save_json(self.encoder, out_src_vocab_file) save_json(self.target_encoder, out_tgt_vocab_file) saved_files.append(out_src_vocab_file) saved_files.append(out_tgt_vocab_file) else: out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] ) save_json(self.encoder, out_vocab_file) saved_files.append(out_vocab_file) for spm_save_filename, spm_orig_path, spm_model in zip( [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]], self.spm_files, [self.spm_source, self.spm_target], ): spm_save_path = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + spm_save_filename ) if os.path.abspath(spm_orig_path) != os.path.abspath(spm_save_path) and os.path.isfile(spm_orig_path): copyfile(spm_orig_path, spm_save_path) saved_files.append(spm_save_path) elif not os.path.isfile(spm_orig_path): with open(spm_save_path, "wb") as fi: content_spiece_model = spm_model.serialized_model_proto() fi.write(content_spiece_model) saved_files.append(spm_save_path) return tuple(saved_files) def get_vocab(self) -> Dict: return self.get_src_vocab() def get_src_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) def get_tgt_vocab(self): return dict(self.target_encoder, **self.added_tokens_decoder) def __getstate__(self) -> Dict: state = self.__dict__.copy() state.update( {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]} ) return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.spm_source, self.spm_target = (load_spm(f, self.sp_model_kwargs) for f in self.spm_files) self.current_spm = self.spm_source self._setup_normalizer() def num_special_tokens_to_add(self, *args, **kwargs): """Just EOS""" return 1 def _special_token_mask(self, seq): all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special return [1 if x in all_special_ids else 0 for x in seq] def get_special_tokens_mask( self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False ) -> List[int]: """Get list where entries are [1] if a token is [eos] or [pad] else 0.""" if already_has_special_tokens: return self._special_token_mask(token_ids_0) elif token_ids_1 is None: return self._special_token_mask(token_ids_0) + [1] else: return self._special_token_mask(token_ids_0 + token_ids_1) + [1] def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor: spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs) spm.Load(path) return spm def save_json(data, path: str) -> None: with open(path, "w") as f: json.dump(data, f, indent=2) def load_json(path: str) -> Union[Dict, List]: with open(path, "r") as f: return json.load(f)

transformers/src/transformers/models/marian/tokenization_marian.py/0

{ "file_path": "transformers/src/transformers/models/marian/tokenization_marian.py", "repo_id": "transformers", "token_count": 7701 }

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# coding=utf-8 # Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys from argparse import ArgumentParser from dataclasses import dataclass from pathlib import Path from pprint import pformat from typing import Any, Dict, Iterator, List, Set, Tuple import requests import torch import torchvision.transforms as T from detectron2.checkpoint import DetectionCheckpointer from detectron2.config import get_cfg from detectron2.data import MetadataCatalog from detectron2.projects.deeplab import add_deeplab_config from PIL import Image from torch import Tensor, nn from transformers.models.maskformer.feature_extraction_maskformer import MaskFormerImageProcessor from transformers.models.maskformer.modeling_maskformer import ( MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerForInstanceSegmentationOutput, MaskFormerModel, MaskFormerModelOutput, ) from transformers.utils import logging StateDict = Dict[str, Tensor] logging.set_verbosity_info() logger = logging.get_logger() torch.manual_seed(0) class TrackedStateDict: def __init__(self, to_track: Dict): """This class "tracks" a python dictionary by keeping track of which item is accessed. Args: to_track (Dict): The dictionary we wish to track """ self.to_track = to_track self._seen: Set[str] = set() def __getitem__(self, key: str) -> Any: return self.to_track[key] def __setitem__(self, key: str, item: Any): self._seen.add(key) self.to_track[key] = item def diff(self) -> List[str]: """This method returns a set difference between the keys in the tracked state dict and the one we have access so far. This is an effective method to check if we have update all the keys Returns: List[str]: List of keys not yet updated """ return set(self.to_track.keys()) - self._seen def copy(self) -> Dict: # proxy the call to the internal dictionary return self.to_track.copy() # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" img_data = requests.get(url, stream=True).raw im = Image.open(img_data) return im @dataclass class Args: """Fake command line arguments needed by maskformer/detectron implementation""" config_file: str def setup_cfg(args: Args): # load config from file and command-line arguments cfg = get_cfg() add_deeplab_config(cfg) add_mask_former_config(cfg) cfg.merge_from_file(args.config_file) cfg.freeze() return cfg class OriginalMaskFormerConfigToOursConverter: def __call__(self, original_config: object) -> MaskFormerConfig: model = original_config.MODEL mask_former = model.MASK_FORMER swin = model.SWIN dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0]) id2label = dict(enumerate(dataset_catalog.stuff_classes)) label2id = {label: idx for idx, label in id2label.items()} config: MaskFormerConfig = MaskFormerConfig( fpn_feature_size=model.SEM_SEG_HEAD.CONVS_DIM, mask_feature_size=model.SEM_SEG_HEAD.MASK_DIM, num_labels=model.SEM_SEG_HEAD.NUM_CLASSES, no_object_weight=mask_former.NO_OBJECT_WEIGHT, num_queries=mask_former.NUM_OBJECT_QUERIES, backbone_config={ "pretrain_img_size": swin.PRETRAIN_IMG_SIZE, "image_size": swin.PRETRAIN_IMG_SIZE, "in_channels": 3, "patch_size": swin.PATCH_SIZE, "embed_dim": swin.EMBED_DIM, "depths": swin.DEPTHS, "num_heads": swin.NUM_HEADS, "window_size": swin.WINDOW_SIZE, "drop_path_rate": swin.DROP_PATH_RATE, "model_type": "swin", }, dice_weight=mask_former.DICE_WEIGHT, ce_weight=1.0, mask_weight=mask_former.MASK_WEIGHT, decoder_config={ "model_type": "detr", "max_position_embeddings": 1024, "encoder_layers": 6, "encoder_ffn_dim": 2048, "encoder_attention_heads": 8, "decoder_layers": mask_former.DEC_LAYERS, "decoder_ffn_dim": mask_former.DIM_FEEDFORWARD, "decoder_attention_heads": mask_former.NHEADS, "encoder_layerdrop": 0.0, "decoder_layerdrop": 0.0, "d_model": mask_former.HIDDEN_DIM, "dropout": mask_former.DROPOUT, "attention_dropout": 0.0, "activation_dropout": 0.0, "init_std": 0.02, "init_xavier_std": 1.0, "scale_embedding": False, "auxiliary_loss": False, "dilation": False, # default pretrained config values }, id2label=id2label, label2id=label2id, ) return config class OriginalMaskFormerConfigToImageProcessorConverter: def __call__(self, original_config: object) -> MaskFormerImageProcessor: model = original_config.MODEL model_input = original_config.INPUT dataset_catalog = MetadataCatalog.get(original_config.DATASETS.TEST[0]) return MaskFormerImageProcessor( image_mean=(torch.tensor(model.PIXEL_MEAN) / 255).tolist(), image_std=(torch.tensor(model.PIXEL_STD) / 255).tolist(), size=model_input.MIN_SIZE_TEST, max_size=model_input.MAX_SIZE_TEST, num_labels=model.SEM_SEG_HEAD.NUM_CLASSES, ignore_index=dataset_catalog.ignore_label, size_divisibility=32, # 32 is required by swin ) class OriginalMaskFormerCheckpointToOursConverter: def __init__(self, original_model: nn.Module, config: MaskFormerConfig): self.original_model = original_model self.config = config def pop_all(self, renamed_keys: List[Tuple[str, str]], dst_state_dict: StateDict, src_state_dict: StateDict): for src_key, dst_key in renamed_keys: dst_state_dict[dst_key] = src_state_dict.pop(src_key) def replace_backbone(self, dst_state_dict: StateDict, src_state_dict: StateDict, config: MaskFormerConfig): dst_prefix: str = "pixel_level_module.encoder" src_prefix: str = "backbone" renamed_keys = [ ( f"{src_prefix}.patch_embed.proj.weight", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.weight", ), (f"{src_prefix}.patch_embed.proj.bias", f"{dst_prefix}.model.embeddings.patch_embeddings.projection.bias"), (f"{src_prefix}.patch_embed.norm.weight", f"{dst_prefix}.model.embeddings.norm.weight"), (f"{src_prefix}.patch_embed.norm.bias", f"{dst_prefix}.model.embeddings.norm.bias"), ] num_layers = len(config.backbone_config.depths) for layer_idx in range(num_layers): for block_idx in range(config.backbone_config.depths[layer_idx]): renamed_keys.extend( [ # src, dst ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm1.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_before.bias", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_bias_table", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_bias_table", ), ] ) # now we need to handle the attentions # read in weights + bias of input projection layer of cross-attention src_att_weight = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight"] src_att_bias = src_state_dict[f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias"] size = src_att_weight.shape[0] offset = size // 3 dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.weight" ] = src_att_weight[:offset, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.query.bias" ] = src_att_bias[:offset] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.weight" ] = src_att_weight[offset : offset * 2, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.key.bias" ] = src_att_bias[offset : offset * 2] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.weight" ] = src_att_weight[-offset:, :] dst_state_dict[ f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.value.bias" ] = src_att_bias[-offset:] # let's pop them src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.weight") src_state_dict.pop(f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.qkv.bias") # proj renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.proj.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.output.dense.bias", ), ] ) # second norm renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.norm2.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.layernorm_after.bias", ), ] ) # mlp renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc1.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.intermediate.dense.bias", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.weight", ), ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.mlp.fc2.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.output.dense.bias", ), ] ) renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.blocks.{block_idx}.attn.relative_position_index", f"{dst_prefix}.model.encoder.layers.{layer_idx}.blocks.{block_idx}.attention.self.relative_position_index", ) ] ) if layer_idx < num_layers - 1: # patch merging renamed_keys.extend( [ ( f"{src_prefix}.layers.{layer_idx}.downsample.reduction.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.reduction.weight", ), ( f"{src_prefix}.layers.{layer_idx}.downsample.norm.weight", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.weight", ), ( f"{src_prefix}.layers.{layer_idx}.downsample.norm.bias", f"{dst_prefix}.model.encoder.layers.{layer_idx}.downsample.norm.bias", ), ] ) # hidden states norms renamed_keys.extend( [ ( f"{src_prefix}.norm{layer_idx}.weight", f"{dst_prefix}.hidden_states_norms.{layer_idx}.weight", ), ( f"{src_prefix}.norm{layer_idx}.bias", f"{dst_prefix}.hidden_states_norms.{layer_idx}.bias", ), ] ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def replace_pixel_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "pixel_level_module.decoder" src_prefix: str = "sem_seg_head.pixel_decoder" self.replace_backbone(dst_state_dict, src_state_dict, self.config) def rename_keys_for_conv(detectron_conv: str, mine_conv: str): return [ (f"{detectron_conv}.weight", f"{mine_conv}.0.weight"), # 2 cuz the have act in the middle -> rename it (f"{detectron_conv}.norm.weight", f"{mine_conv}.1.weight"), (f"{detectron_conv}.norm.bias", f"{mine_conv}.1.bias"), ] renamed_keys = [ (f"{src_prefix}.mask_features.weight", f"{dst_prefix}.mask_projection.weight"), (f"{src_prefix}.mask_features.bias", f"{dst_prefix}.mask_projection.bias"), # the layers in the original one are in reverse order, stem is the last one! ] renamed_keys.extend(rename_keys_for_conv(f"{src_prefix}.layer_4", f"{dst_prefix}.fpn.stem")) # add all the fpn layers (here we need some config parameters to know the size in advance) for src_i, dst_i in zip(range(3, 0, -1), range(0, 3)): renamed_keys.extend( rename_keys_for_conv(f"{src_prefix}.adapter_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.proj") ) renamed_keys.extend( rename_keys_for_conv(f"{src_prefix}.layer_{src_i}", f"{dst_prefix}.fpn.layers.{dst_i}.block") ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def rename_keys_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" # not sure why we are not popping direcetly here! # here we list all keys to be renamed (original name on the left, our name on the right) rename_keys = [] for i in range(self.config.decoder_config.decoder_layers): # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms rename_keys.append( ( f"{src_prefix}.layers.{i}.self_attn.out_proj.weight", f"{dst_prefix}.layers.{i}.self_attn.out_proj.weight", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.self_attn.out_proj.bias", f"{dst_prefix}.layers.{i}.self_attn.out_proj.bias", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.multihead_attn.out_proj.weight", f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.weight", ) ) rename_keys.append( ( f"{src_prefix}.layers.{i}.multihead_attn.out_proj.bias", f"{dst_prefix}.layers.{i}.encoder_attn.out_proj.bias", ) ) rename_keys.append((f"{src_prefix}.layers.{i}.linear1.weight", f"{dst_prefix}.layers.{i}.fc1.weight")) rename_keys.append((f"{src_prefix}.layers.{i}.linear1.bias", f"{dst_prefix}.layers.{i}.fc1.bias")) rename_keys.append((f"{src_prefix}.layers.{i}.linear2.weight", f"{dst_prefix}.layers.{i}.fc2.weight")) rename_keys.append((f"{src_prefix}.layers.{i}.linear2.bias", f"{dst_prefix}.layers.{i}.fc2.bias")) rename_keys.append( (f"{src_prefix}.layers.{i}.norm1.weight", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm1.bias", f"{dst_prefix}.layers.{i}.self_attn_layer_norm.bias") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm2.weight", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm2.bias", f"{dst_prefix}.layers.{i}.encoder_attn_layer_norm.bias") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm3.weight", f"{dst_prefix}.layers.{i}.final_layer_norm.weight") ) rename_keys.append( (f"{src_prefix}.layers.{i}.norm3.bias", f"{dst_prefix}.layers.{i}.final_layer_norm.bias") ) return rename_keys def replace_q_k_v_in_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" for i in range(self.config.decoder_config.decoder_layers): # read in weights + bias of input projection layer of self-attention in_proj_weight = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_weight") in_proj_bias = src_state_dict.pop(f"{src_prefix}.layers.{i}.self_attn.in_proj_bias") # next, add query, keys and values (in that order) to the state dict dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] dst_state_dict[f"{dst_prefix}.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] # read in weights + bias of input projection layer of cross-attention in_proj_weight_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_weight") in_proj_bias_cross_attn = src_state_dict.pop(f"{src_prefix}.layers.{i}.multihead_attn.in_proj_bias") # next, add query, keys and values (in that order) of cross-attention to the state dict dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.weight"] = in_proj_weight_cross_attn[:256, :] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.q_proj.bias"] = in_proj_bias_cross_attn[:256] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.weight"] = in_proj_weight_cross_attn[ 256:512, : ] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.k_proj.bias"] = in_proj_bias_cross_attn[256:512] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.weight"] = in_proj_weight_cross_attn[-256:, :] dst_state_dict[f"{dst_prefix}.layers.{i}.encoder_attn.v_proj.bias"] = in_proj_bias_cross_attn[-256:] def replace_detr_decoder(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module.decoder" src_prefix: str = "sem_seg_head.predictor.transformer.decoder" renamed_keys = self.rename_keys_in_detr_decoder(dst_state_dict, src_state_dict) # add more renamed_keys.extend( [ (f"{src_prefix}.norm.weight", f"{dst_prefix}.layernorm.weight"), (f"{src_prefix}.norm.bias", f"{dst_prefix}.layernorm.bias"), ] ) self.pop_all(renamed_keys, dst_state_dict, src_state_dict) self.replace_q_k_v_in_detr_decoder(dst_state_dict, src_state_dict) def replace_transformer_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): dst_prefix: str = "transformer_module" src_prefix: str = "sem_seg_head.predictor" self.replace_detr_decoder(dst_state_dict, src_state_dict) renamed_keys = [ (f"{src_prefix}.query_embed.weight", f"{dst_prefix}.queries_embedder.weight"), (f"{src_prefix}.input_proj.weight", f"{dst_prefix}.input_projection.weight"), (f"{src_prefix}.input_proj.bias", f"{dst_prefix}.input_projection.bias"), ] self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def replace_instance_segmentation_module(self, dst_state_dict: StateDict, src_state_dict: StateDict): # NOTE in our case we don't have a prefix, thus we removed the "." from the keys later on! dst_prefix: str = "" src_prefix: str = "sem_seg_head.predictor" renamed_keys = [ (f"{src_prefix}.class_embed.weight", f"{dst_prefix}class_predictor.weight"), (f"{src_prefix}.class_embed.bias", f"{dst_prefix}class_predictor.bias"), ] mlp_len = 3 for i in range(mlp_len): renamed_keys.extend( [ (f"{src_prefix}.mask_embed.layers.{i}.weight", f"{dst_prefix}mask_embedder.{i}.0.weight"), (f"{src_prefix}.mask_embed.layers.{i}.bias", f"{dst_prefix}mask_embedder.{i}.0.bias"), ] ) logger.info(f"Replacing keys {pformat(renamed_keys)}") self.pop_all(renamed_keys, dst_state_dict, src_state_dict) def convert(self, mask_former: MaskFormerModel) -> MaskFormerModel: dst_state_dict = TrackedStateDict(mask_former.state_dict()) src_state_dict = self.original_model.state_dict() self.replace_pixel_module(dst_state_dict, src_state_dict) self.replace_transformer_module(dst_state_dict, src_state_dict) logger.info(f"Missed keys are {pformat(dst_state_dict.diff())}") logger.info(f"Not copied keys are {pformat(src_state_dict.keys())}") logger.info("🙌 Done") mask_former.load_state_dict(dst_state_dict) return mask_former def convert_instance_segmentation( self, mask_former: MaskFormerForInstanceSegmentation ) -> MaskFormerForInstanceSegmentation: dst_state_dict = TrackedStateDict(mask_former.state_dict()) src_state_dict = self.original_model.state_dict() self.replace_instance_segmentation_module(dst_state_dict, src_state_dict) mask_former.load_state_dict(dst_state_dict) return mask_former @staticmethod def using_dirs(checkpoints_dir: Path, config_dir: Path) -> Iterator[Tuple[object, Path, Path]]: checkpoints: List[Path] = checkpoints_dir.glob("**/*.pkl") for checkpoint in checkpoints: logger.info(f"💪 Converting {checkpoint.stem}") # find associated config file config: Path = config_dir / checkpoint.parents[0].stem / "swin" / f"{checkpoint.stem}.yaml" yield config, checkpoint def test(original_model, our_model: MaskFormerForInstanceSegmentation, image_processor: MaskFormerImageProcessor): with torch.no_grad(): original_model = original_model.eval() our_model = our_model.eval() im = prepare_img() tr = T.Compose( [ T.Resize((384, 384)), T.ToTensor(), T.Normalize( mean=torch.tensor([123.675, 116.280, 103.530]) / 255.0, std=torch.tensor([58.395, 57.120, 57.375]) / 255.0, ), ], ) x = tr(im).unsqueeze(0) original_model_backbone_features = original_model.backbone(x.clone()) our_model_output: MaskFormerModelOutput = our_model.model(x.clone(), output_hidden_states=True) for original_model_feature, our_model_feature in zip( original_model_backbone_features.values(), our_model_output.encoder_hidden_states ): assert torch.allclose( original_model_feature, our_model_feature, atol=1e-3 ), "The backbone features are not the same." original_model_pixel_out = original_model.sem_seg_head.pixel_decoder.forward_features( original_model_backbone_features ) assert torch.allclose( original_model_pixel_out[0], our_model_output.pixel_decoder_last_hidden_state, atol=1e-4 ), "The pixel decoder feature are not the same" # let's test the full model original_model_out = original_model([{"image": x.squeeze(0)}]) original_segmentation = original_model_out[0]["sem_seg"] our_model_out: MaskFormerForInstanceSegmentationOutput = our_model(x) our_segmentation = image_processor.post_process_segmentation(our_model_out, target_size=(384, 384)) assert torch.allclose( original_segmentation, our_segmentation, atol=1e-3 ), "The segmentation image is not the same." logger.info("✅ Test passed!") def get_name(checkpoint_file: Path): model_name_raw: str = checkpoint_file.stem # model_name_raw is something like maskformer_panoptic_swin_base_IN21k_384_bs64_554k parent_name: str = checkpoint_file.parents[0].stem backbone = "swin" dataset = "" if "coco" in parent_name: dataset = "coco" elif "ade" in parent_name: dataset = "ade" else: raise ValueError(f"{parent_name} must be wrong since we didn't find 'coco' or 'ade' in it ") backbone_types = ["tiny", "small", "base", "large"] backbone_type = list(filter(lambda x: x in model_name_raw, backbone_types))[0] model_name = f"maskformer-{backbone}-{backbone_type}-{dataset}" return model_name if __name__ == "__main__": parser = ArgumentParser( description="Command line to convert the original maskformers (with swin backbone) to our implementations." ) parser.add_argument( "--checkpoints_dir", type=Path, help=( "A directory containing the model's checkpoints. The directory has to have the following structure:" " <DIR_NAME>/<DATASET_NAME>/<CONFIG_NAME>.pkl" ), ) parser.add_argument( "--configs_dir", type=Path, help=( "A directory containing the model's configs, see detectron2 doc. The directory has to have the following" " structure: <DIR_NAME>/<DATASET_NAME>/<CONFIG_NAME>.yaml" ), ) parser.add_argument( "--pytorch_dump_folder_path", required=True, type=Path, help="Path to the folder to output PyTorch models.", ) parser.add_argument( "--maskformer_dir", required=True, type=Path, help=( "A path to MaskFormer's original implementation directory. You can download from here:" " https://github.com/facebookresearch/MaskFormer" ), ) args = parser.parse_args() checkpoints_dir: Path = args.checkpoints_dir config_dir: Path = args.configs_dir save_directory: Path = args.pytorch_dump_folder_path maskformer_dir: Path = args.maskformer_dir # append the path to the parents to maskformer dir sys.path.append(str(maskformer_dir.parent)) # and import what's needed from MaskFormer.mask_former import add_mask_former_config from MaskFormer.mask_former.mask_former_model import MaskFormer as OriginalMaskFormer if not save_directory.exists(): save_directory.mkdir(parents=True) for config_file, checkpoint_file in OriginalMaskFormerCheckpointToOursConverter.using_dirs( checkpoints_dir, config_dir ): image_processor = OriginalMaskFormerConfigToImageProcessorConverter()(setup_cfg(Args(config_file=config_file))) original_config = setup_cfg(Args(config_file=config_file)) mask_former_kwargs = OriginalMaskFormer.from_config(original_config) original_model = OriginalMaskFormer(**mask_former_kwargs).eval() DetectionCheckpointer(original_model).load(str(checkpoint_file)) config: MaskFormerConfig = OriginalMaskFormerConfigToOursConverter()(original_config) mask_former = MaskFormerModel(config=config).eval() converter = OriginalMaskFormerCheckpointToOursConverter(original_model, config) maskformer = converter.convert(mask_former) mask_former_for_instance_segmentation = MaskFormerForInstanceSegmentation(config=config).eval() mask_former_for_instance_segmentation.model = mask_former mask_former_for_instance_segmentation = converter.convert_instance_segmentation( mask_former_for_instance_segmentation ) test(original_model, mask_former_for_instance_segmentation, image_processor) model_name = get_name(checkpoint_file) logger.info(f"🪄 Saving {model_name}") image_processor.save_pretrained(save_directory / model_name) mask_former_for_instance_segmentation.save_pretrained(save_directory / model_name) image_processor.push_to_hub( repo_path_or_name=save_directory / model_name, commit_message="Add model", use_temp_dir=True, ) mask_former_for_instance_segmentation.push_to_hub( repo_path_or_name=save_directory / model_name, commit_message="Add model", use_temp_dir=True, )

transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 16104 }

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# coding=utf-8 # Copyright 2021 The Facebook AI Research Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/mbart-large-50-one-to-many-mmt": ( "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "facebook/mbart-large-50-one-to-many-mmt": 1024, } FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"] # fmt: skip class MBart50Tokenizer(PreTrainedTokenizer): """ Construct a MBart50 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. src_lang (`str`, *optional*): A string representing the source language. tgt_lang (`str`, *optional*): A string representing the target language. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. Examples: ```python >>> from transformers import MBart50Tokenizer >>> tokenizer = MBart50Tokenizer.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO") >>> src_text = " UN Chief Says There Is No Military Solution in Syria" >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria" >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt") >>> # model(**model_inputs) should work ```""" vocab_files_names = VOCAB_FILES_NAMES max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, src_lang=None, tgt_lang=None, eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or [] kwargs["additional_special_tokens"] += [ code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"] ] self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.sp_model_size = len(self.sp_model) self.lang_code_to_id = { code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES) } self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()} self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset self.fairseq_tokens_to_ids.update(self.lang_code_to_id) self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} super().__init__( src_lang=src_lang, tgt_lang=tgt_lang, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) self._src_lang = src_lang if src_lang is not None else "en_XX" self.cur_lang_code_id = self.lang_code_to_id[self._src_lang] self.tgt_lang = tgt_lang self.set_src_lang_special_tokens(self._src_lang) @property def vocab_size(self) -> int: return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token @property def src_lang(self) -> str: return self._src_lang @src_lang.setter def src_lang(self, new_src_lang: str) -> None: self._src_lang = new_src_lang self.set_src_lang_special_tokens(self._src_lang) def __getstate__(self) -> Dict: state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d: Dict) -> None: self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def get_vocab(self) -> Dict: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def _tokenize(self, text: str) -> List[str]: return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset) # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.convert_tokens_to_string def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" prev_is_special = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) prefix_ones = [1] * len(self.prefix_tokens) suffix_ones = [1] * len(self.suffix_tokens) if token_ids_1 is None: return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An MBART-50 sequence has the following format, where `X` represents the sequence: - `input_ids` (for encoder) `[src_lang_code] X [eos]` - `labels`: (for decoder) `[tgt_lang_code] X [eos]` BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens def _build_translation_inputs( self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs ): """Used by translation pipeline, to prepare inputs for the generate function""" if src_lang is None or tgt_lang is None: raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") self.src_lang = src_lang inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) inputs["forced_bos_token_id"] = tgt_lang_id return inputs def prepare_seq2seq_batch( self, src_texts: List[str], src_lang: str = "en_XX", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "ro_RO", **kwargs, ) -> BatchEncoding: self.src_lang = src_lang self.tgt_lang = tgt_lang return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) def _switch_to_input_mode(self): return self.set_src_lang_special_tokens(self.src_lang) def _switch_to_target_mode(self): return self.set_tgt_lang_special_tokens(self.tgt_lang) def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos].""" self.cur_lang_code_id = self.lang_code_to_id[src_lang] self.prefix_tokens = [self.cur_lang_code_id] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos].""" self.cur_lang_code_id = self.lang_code_to_id[tgt_lang] self.prefix_tokens = [self.cur_lang_code_id] self.suffix_tokens = [self.eos_token_id]

transformers/src/transformers/models/mbart50/tokenization_mbart50.py/0

{ "file_path": "transformers/src/transformers/models/mbart50/tokenization_mbart50.py", "repo_id": "transformers", "token_count": 7343 }

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# coding=utf-8 # Copyright 2023 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 MGP-STR.""" import warnings from transformers import AutoTokenizer from transformers.utils import is_torch_available from transformers.utils.generic import ExplicitEnum from ...processing_utils import ProcessorMixin if is_torch_available(): import torch class DecodeType(ExplicitEnum): CHARACTER = "char" BPE = "bpe" WORDPIECE = "wp" SUPPORTED_ANNOTATION_FORMATS = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE) class MgpstrProcessor(ProcessorMixin): r""" Constructs a MGP-STR processor which wraps an image processor and MGP-STR tokenizers into a single [`MgpstrProcessor`] offers all the functionalities of `ViTImageProcessor`] and [`MgpstrTokenizer`]. See the [`~MgpstrProcessor.__call__`] and [`~MgpstrProcessor.batch_decode`] for more information. Args: image_processor (`ViTImageProcessor`, *optional*): An instance of `ViTImageProcessor`. The image processor is a required input. tokenizer ([`MgpstrTokenizer`], *optional*): The tokenizer is a required input. """ attributes = ["image_processor", "char_tokenizer"] image_processor_class = "ViTImageProcessor" char_tokenizer_class = "MgpstrTokenizer" def __init__(self, image_processor=None, tokenizer=None, **kwargs): feature_extractor = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead.", FutureWarning, ) feature_extractor = kwargs.pop("feature_extractor") image_processor = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") self.char_tokenizer = tokenizer self.bpe_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") self.wp_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") super().__init__(image_processor, tokenizer) def __call__(self, text=None, images=None, return_tensors=None, **kwargs): """ When used in normal mode, this method forwards all its arguments to ViTImageProcessor's [`~ViTImageProcessor.__call__`] and returns its output. This method also forwards the `text` and `kwargs` arguments to MgpstrTokenizer's [`~MgpstrTokenizer.__call__`] if `text` is not `None` to encode the text. Please refer to the doctsring of the above methods for more information. """ if images is None and text is None: raise ValueError("You need to specify either an `images` or `text` input to process.") if images is not None: inputs = self.image_processor(images, return_tensors=return_tensors, **kwargs) if text is not None: encodings = self.char_tokenizer(text, return_tensors=return_tensors, **kwargs) if text is None: return inputs elif images is None: return encodings else: inputs["labels"] = encodings["input_ids"] return inputs def batch_decode(self, sequences): """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`torch.Tensor`): List of tokenized input ids. Returns: `Dict[str, any]`: Dictionary of all the outputs of the decoded results. generated_text (`List[str]`): The final results after fusion of char, bpe, and wp. scores (`List[float]`): The final scores after fusion of char, bpe, and wp. char_preds (`List[str]`): The list of character decoded sentences. bpe_preds (`List[str]`): The list of bpe decoded sentences. wp_preds (`List[str]`): The list of wp decoded sentences. This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ char_preds, bpe_preds, wp_preds = sequences batch_size = char_preds.size(0) char_strs, char_scores = self._decode_helper(char_preds, "char") bpe_strs, bpe_scores = self._decode_helper(bpe_preds, "bpe") wp_strs, wp_scores = self._decode_helper(wp_preds, "wp") final_strs = [] final_scores = [] for i in range(batch_size): scores = [char_scores[i], bpe_scores[i], wp_scores[i]] strs = [char_strs[i], bpe_strs[i], wp_strs[i]] max_score_index = scores.index(max(scores)) final_strs.append(strs[max_score_index]) final_scores.append(scores[max_score_index]) out = {} out["generated_text"] = final_strs out["scores"] = final_scores out["char_preds"] = char_strs out["bpe_preds"] = bpe_strs out["wp_preds"] = wp_strs return out def _decode_helper(self, pred_logits, format): """ Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer. Args: pred_logits (`torch.Tensor`): List of model prediction logits. format (`Union[DecoderType, str]`): Type of model prediction. Must be one of ['char', 'bpe', 'wp']. Returns: `tuple`: dec_strs(`str`): The decode strings of model prediction. conf_scores(`List[float]`): The confidence score of model prediction. """ if format == DecodeType.CHARACTER: decoder = self.char_decode eos_token = 1 eos_str = "[s]" elif format == DecodeType.BPE: decoder = self.bpe_decode eos_token = 2 eos_str = "#" elif format == DecodeType.WORDPIECE: decoder = self.wp_decode eos_token = 102 eos_str = "[SEP]" else: raise ValueError(f"Format {format} is not supported.") dec_strs, conf_scores = [], [] batch_size = pred_logits.size(0) batch_max_length = pred_logits.size(1) _, preds_index = pred_logits.topk(1, dim=-1, largest=True, sorted=True) preds_index = preds_index.view(-1, batch_max_length)[:, 1:] preds_str = decoder(preds_index) preds_max_prob, _ = torch.nn.functional.softmax(pred_logits, dim=2).max(dim=2) preds_max_prob = preds_max_prob[:, 1:] for index in range(batch_size): pred_eos = preds_str[index].find(eos_str) pred = preds_str[index][:pred_eos] pred_index = preds_index[index].cpu().tolist() pred_eos_index = pred_index.index(eos_token) if eos_token in pred_index else -1 pred_max_prob = preds_max_prob[index][: pred_eos_index + 1] confidence_score = pred_max_prob.cumprod(dim=0)[-1] if pred_max_prob.nelement() != 0 else 0.0 dec_strs.append(pred) conf_scores.append(confidence_score) return dec_strs, conf_scores def char_decode(self, sequences): """ Convert a list of lists of char token ids into a list of strings by calling char tokenizer. Args: sequences (`torch.Tensor`): List of tokenized input ids. Returns: `List[str]`: The list of char decoded sentences. """ decode_strs = [seq.replace(" ", "") for seq in self.char_tokenizer.batch_decode(sequences)] return decode_strs def bpe_decode(self, sequences): """ Convert a list of lists of bpe token ids into a list of strings by calling bpe tokenizer. Args: sequences (`torch.Tensor`): List of tokenized input ids. Returns: `List[str]`: The list of bpe decoded sentences. """ return self.bpe_tokenizer.batch_decode(sequences) def wp_decode(self, sequences): """ Convert a list of lists of word piece token ids into a list of strings by calling word piece tokenizer. Args: sequences (`torch.Tensor`): List of tokenized input ids. Returns: `List[str]`: The list of wp decoded sentences. """ decode_strs = [seq.replace(" ", "") for seq in self.wp_tokenizer.batch_decode(sequences)] return decode_strs

transformers/src/transformers/models/mgp_str/processing_mgp_str.py/0

{ "file_path": "transformers/src/transformers/models/mgp_str/processing_mgp_str.py", "repo_id": "transformers", "token_count": 4028 }

321

# coding=utf-8 # Copyright 2022 Apple Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch MobileNetV2 model.""" from typing import Optional, Union import torch from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, SemanticSegmenterOutput, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_mobilenet_v2 import MobileNetV2Config logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "MobileNetV2Config" # Base docstring _CHECKPOINT_FOR_DOC = "google/mobilenet_v2_1.0_224" _EXPECTED_OUTPUT_SHAPE = [1, 1280, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/mobilenet_v2_1.0_224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/mobilenet_v2_1.4_224", "google/mobilenet_v2_1.0_224", "google/mobilenet_v2_0.37_160", "google/mobilenet_v2_0.35_96", # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2 ] def _build_tf_to_pytorch_map(model, config, tf_weights=None): """ A map of modules from TF to PyTorch. """ tf_to_pt_map = {} if isinstance(model, (MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation)): backbone = model.mobilenet_v2 else: backbone = model # Use the EMA weights if available def ema(x): return x + "/ExponentialMovingAverage" if x + "/ExponentialMovingAverage" in tf_weights else x prefix = "MobilenetV2/Conv/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.first_conv.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.first_conv.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.first_conv.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.first_conv.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.first_conv.normalization.running_var prefix = "MobilenetV2/expanded_conv/depthwise/" tf_to_pt_map[ema(prefix + "depthwise_weights")] = backbone.conv_stem.conv_3x3.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.conv_3x3.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.conv_3x3.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.conv_3x3.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.conv_3x3.normalization.running_var prefix = "MobilenetV2/expanded_conv/project/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_stem.reduce_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_stem.reduce_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_stem.reduce_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_stem.reduce_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_stem.reduce_1x1.normalization.running_var for i in range(16): tf_index = i + 1 pt_index = i pointer = backbone.layer[pt_index] prefix = f"MobilenetV2/expanded_conv_{tf_index}/expand/" tf_to_pt_map[ema(prefix + "weights")] = pointer.expand_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.expand_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.expand_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.expand_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.expand_1x1.normalization.running_var prefix = f"MobilenetV2/expanded_conv_{tf_index}/depthwise/" tf_to_pt_map[ema(prefix + "depthwise_weights")] = pointer.conv_3x3.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.conv_3x3.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.conv_3x3.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.conv_3x3.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.conv_3x3.normalization.running_var prefix = f"MobilenetV2/expanded_conv_{tf_index}/project/" tf_to_pt_map[ema(prefix + "weights")] = pointer.reduce_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = pointer.reduce_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = pointer.reduce_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = pointer.reduce_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = pointer.reduce_1x1.normalization.running_var prefix = "MobilenetV2/Conv_1/" tf_to_pt_map[ema(prefix + "weights")] = backbone.conv_1x1.convolution.weight tf_to_pt_map[ema(prefix + "BatchNorm/beta")] = backbone.conv_1x1.normalization.bias tf_to_pt_map[ema(prefix + "BatchNorm/gamma")] = backbone.conv_1x1.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = backbone.conv_1x1.normalization.running_mean tf_to_pt_map[prefix + "BatchNorm/moving_variance"] = backbone.conv_1x1.normalization.running_var if isinstance(model, MobileNetV2ForImageClassification): prefix = "MobilenetV2/Logits/Conv2d_1c_1x1/" tf_to_pt_map[ema(prefix + "weights")] = model.classifier.weight tf_to_pt_map[ema(prefix + "biases")] = model.classifier.bias if isinstance(model, MobileNetV2ForSemanticSegmentation): prefix = "image_pooling/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_pool.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_pool.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_pool.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_pool.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_pool.normalization.running_var prefix = "aspp0/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_aspp.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_aspp.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_aspp.normalization.weight tf_to_pt_map[prefix + "BatchNorm/moving_mean"] = model.segmentation_head.conv_aspp.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_aspp.normalization.running_var prefix = "concat_projection/" tf_to_pt_map[prefix + "weights"] = model.segmentation_head.conv_projection.convolution.weight tf_to_pt_map[prefix + "BatchNorm/beta"] = model.segmentation_head.conv_projection.normalization.bias tf_to_pt_map[prefix + "BatchNorm/gamma"] = model.segmentation_head.conv_projection.normalization.weight tf_to_pt_map[ prefix + "BatchNorm/moving_mean" ] = model.segmentation_head.conv_projection.normalization.running_mean tf_to_pt_map[ prefix + "BatchNorm/moving_variance" ] = model.segmentation_head.conv_projection.normalization.running_var prefix = "logits/semantic/" tf_to_pt_map[ema(prefix + "weights")] = model.segmentation_head.classifier.convolution.weight tf_to_pt_map[ema(prefix + "biases")] = model.segmentation_head.classifier.convolution.bias return tf_to_pt_map def load_tf_weights_in_mobilenet_v2(model, config, tf_checkpoint_path): """Load TensorFlow checkpoints in a PyTorch model.""" try: import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise # Load weights from TF model init_vars = tf.train.list_variables(tf_checkpoint_path) tf_weights = {} for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_checkpoint_path, name) tf_weights[name] = array # Build TF to PyTorch weights loading map tf_to_pt_map = _build_tf_to_pytorch_map(model, config, tf_weights) for name, pointer in tf_to_pt_map.items(): logger.info(f"Importing {name}") if name not in tf_weights: logger.info(f"{name} not in tf pre-trained weights, skipping") continue array = tf_weights[name] if "depthwise_weights" in name: logger.info("Transposing depthwise") array = np.transpose(array, (2, 3, 0, 1)) elif "weights" in name: logger.info("Transposing") if len(pointer.shape) == 2: # copying into linear layer array = array.squeeze().transpose() else: array = np.transpose(array, (3, 2, 0, 1)) if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") logger.info(f"Initialize PyTorch weight {name} {array.shape}") pointer.data = torch.from_numpy(array) tf_weights.pop(name, None) tf_weights.pop(name + "/RMSProp", None) tf_weights.pop(name + "/RMSProp_1", None) tf_weights.pop(name + "/ExponentialMovingAverage", None) tf_weights.pop(name + "/Momentum", None) logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}") return model def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int: """ Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the original TensorFlow repo. It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py """ if min_value is None: min_value = divisor new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) # Make sure that round down does not go down by more than 10%. if new_value < 0.9 * value: new_value += divisor return int(new_value) def apply_depth_multiplier(config: MobileNetV2Config, channels: int) -> int: return make_divisible(int(round(channels * config.depth_multiplier)), config.depth_divisible_by, config.min_depth) def apply_tf_padding(features: torch.Tensor, conv_layer: nn.Conv2d) -> torch.Tensor: """ Apply TensorFlow-style "SAME" padding to a convolution layer. See the notes at: https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2 """ in_height = int(features.shape[-2]) in_width = int(features.shape[-1]) stride_height, stride_width = conv_layer.stride kernel_height, kernel_width = conv_layer.kernel_size dilation_height, dilation_width = conv_layer.dilation if in_height % stride_height == 0: pad_along_height = max(kernel_height - stride_height, 0) else: pad_along_height = max(kernel_height - (in_height % stride_height), 0) if in_width % stride_width == 0: pad_along_width = max(kernel_width - stride_width, 0) else: pad_along_width = max(kernel_width - (in_width % stride_width), 0) pad_left = pad_along_width // 2 pad_right = pad_along_width - pad_left pad_top = pad_along_height // 2 pad_bottom = pad_along_height - pad_top padding = ( pad_left * dilation_width, pad_right * dilation_width, pad_top * dilation_height, pad_bottom * dilation_height, ) return nn.functional.pad(features, padding, "constant", 0.0) class MobileNetV2ConvLayer(nn.Module): def __init__( self, config: MobileNetV2Config, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, groups: int = 1, bias: bool = False, dilation: int = 1, use_normalization: bool = True, use_activation: Union[bool, str] = True, layer_norm_eps: Optional[float] = None, ) -> None: super().__init__() self.config = config if in_channels % groups != 0: raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups.") if out_channels % groups != 0: raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups.") padding = 0 if config.tf_padding else int((kernel_size - 1) / 2) * dilation self.convolution = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode="zeros", ) if use_normalization: self.normalization = nn.BatchNorm2d( num_features=out_channels, eps=config.layer_norm_eps if layer_norm_eps is None else layer_norm_eps, momentum=0.997, affine=True, track_running_stats=True, ) else: self.normalization = None if use_activation: if isinstance(use_activation, str): self.activation = ACT2FN[use_activation] elif isinstance(config.hidden_act, str): self.activation = ACT2FN[config.hidden_act] else: self.activation = config.hidden_act else: self.activation = None def forward(self, features: torch.Tensor) -> torch.Tensor: if self.config.tf_padding: features = apply_tf_padding(features, self.convolution) features = self.convolution(features) if self.normalization is not None: features = self.normalization(features) if self.activation is not None: features = self.activation(features) return features class MobileNetV2InvertedResidual(nn.Module): def __init__( self, config: MobileNetV2Config, in_channels: int, out_channels: int, stride: int, dilation: int = 1 ) -> None: super().__init__() expanded_channels = make_divisible( int(round(in_channels * config.expand_ratio)), config.depth_divisible_by, config.min_depth ) if stride not in [1, 2]: raise ValueError(f"Invalid stride {stride}.") self.use_residual = (stride == 1) and (in_channels == out_channels) self.expand_1x1 = MobileNetV2ConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=stride, groups=expanded_channels, dilation=dilation, ) self.reduce_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: residual = features features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return residual + features if self.use_residual else features class MobileNetV2Stem(nn.Module): def __init__(self, config: MobileNetV2Config, in_channels: int, expanded_channels: int, out_channels: int) -> None: super().__init__() # The very first layer is a regular 3x3 convolution with stride 2 that expands to 32 channels. # All other expansion layers use the expansion factor to compute the number of output channels. self.first_conv = MobileNetV2ConvLayer( config, in_channels=in_channels, out_channels=expanded_channels, kernel_size=3, stride=2, ) if config.first_layer_is_expansion: self.expand_1x1 = None else: self.expand_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=1 ) self.conv_3x3 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=expanded_channels, kernel_size=3, stride=1, groups=expanded_channels, ) self.reduce_1x1 = MobileNetV2ConvLayer( config, in_channels=expanded_channels, out_channels=out_channels, kernel_size=1, use_activation=False, ) def forward(self, features: torch.Tensor) -> torch.Tensor: features = self.first_conv(features) if self.expand_1x1 is not None: features = self.expand_1x1(features) features = self.conv_3x3(features) features = self.reduce_1x1(features) return features class MobileNetV2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = MobileNetV2Config load_tf_weights = load_tf_weights_in_mobilenet_v2 base_model_prefix = "mobilenet_v2" main_input_name = "pixel_values" supports_gradient_checkpointing = False def _init_weights(self, module: Union[nn.Linear, nn.Conv2d]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.BatchNorm2d): module.bias.data.zero_() module.weight.data.fill_(1.0) MOBILENET_V2_START_DOCSTRING = r""" This model is 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 ([`MobileNetV2Config`]): 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. """ MOBILENET_V2_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`MobileNetV2ImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare MobileNetV2 model outputting raw hidden-states without any specific head on top.", MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2Model(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True): super().__init__(config) self.config = config # Output channels for the projection layers channels = [16, 24, 24, 32, 32, 32, 64, 64, 64, 64, 96, 96, 96, 160, 160, 160, 320] channels = [apply_depth_multiplier(config, x) for x in channels] # Strides for the depthwise layers strides = [2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1] self.conv_stem = MobileNetV2Stem( config, in_channels=config.num_channels, expanded_channels=apply_depth_multiplier(config, 32), out_channels=channels[0], ) current_stride = 2 # first conv layer has stride 2 dilation = 1 self.layer = nn.ModuleList() for i in range(16): # Keep making the feature maps smaller or use dilated convolution? if current_stride == config.output_stride: layer_stride = 1 layer_dilation = dilation dilation *= strides[i] # larger dilation starts in next block else: layer_stride = strides[i] layer_dilation = 1 current_stride *= layer_stride self.layer.append( MobileNetV2InvertedResidual( config, in_channels=channels[i], out_channels=channels[i + 1], stride=layer_stride, dilation=layer_dilation, ) ) if config.finegrained_output and config.depth_multiplier < 1.0: output_channels = 1280 else: output_channels = apply_depth_multiplier(config, 1280) self.conv_1x1 = MobileNetV2ConvLayer( config, in_channels=channels[-1], out_channels=output_channels, kernel_size=1, ) self.pooler = nn.AdaptiveAvgPool2d((1, 1)) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def _prune_heads(self, heads_to_prune): raise NotImplementedError @add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]: 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 pixel_values is None: raise ValueError("You have to specify pixel_values") hidden_states = self.conv_stem(pixel_values) all_hidden_states = () if output_hidden_states else None for i, layer_module in enumerate(self.layer): hidden_states = layer_module(hidden_states) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) last_hidden_state = self.conv_1x1(hidden_states) if self.pooler is not None: pooled_output = torch.flatten(self.pooler(last_hidden_state), start_dim=1) else: pooled_output = None if not return_dict: return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None) return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=all_hidden_states, ) @add_start_docstrings( """ MobileNetV2 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2ForImageClassification(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilenet_v2 = MobileNetV2Model(config) last_hidden_size = self.mobilenet_v2.conv_1x1.convolution.out_channels # Classifier head self.dropout = nn.Dropout(config.classifier_dropout_prob, inplace=True) self.classifier = nn.Linear(last_hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, labels: Optional[torch.Tensor] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutputWithNoAttention]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image 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 outputs = self.mobilenet_v2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(self.dropout(pooled_output)) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention( loss=loss, logits=logits, hidden_states=outputs.hidden_states, ) class MobileNetV2DeepLabV3Plus(nn.Module): """ The neural network from the paper "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" https://arxiv.org/abs/1802.02611 """ def __init__(self, config: MobileNetV2Config) -> None: super().__init__() self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1) self.conv_pool = MobileNetV2ConvLayer( config, in_channels=apply_depth_multiplier(config, 320), out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.conv_aspp = MobileNetV2ConvLayer( config, in_channels=apply_depth_multiplier(config, 320), out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.conv_projection = MobileNetV2ConvLayer( config, in_channels=512, out_channels=256, kernel_size=1, stride=1, use_normalization=True, use_activation="relu", layer_norm_eps=1e-5, ) self.dropout = nn.Dropout2d(config.classifier_dropout_prob) self.classifier = MobileNetV2ConvLayer( config, in_channels=256, out_channels=config.num_labels, kernel_size=1, use_normalization=False, use_activation=False, bias=True, ) def forward(self, features: torch.Tensor) -> torch.Tensor: spatial_size = features.shape[-2:] features_pool = self.avg_pool(features) features_pool = self.conv_pool(features_pool) features_pool = nn.functional.interpolate( features_pool, size=spatial_size, mode="bilinear", align_corners=True ) features_aspp = self.conv_aspp(features) features = torch.cat([features_pool, features_aspp], dim=1) features = self.conv_projection(features) features = self.dropout(features) features = self.classifier(features) return features @add_start_docstrings( """ MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC. """, MOBILENET_V2_START_DOCSTRING, ) class MobileNetV2ForSemanticSegmentation(MobileNetV2PreTrainedModel): def __init__(self, config: MobileNetV2Config) -> None: super().__init__(config) self.num_labels = config.num_labels self.mobilenet_v2 = MobileNetV2Model(config, add_pooling_layer=False) self.segmentation_head = MobileNetV2DeepLabV3Plus(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(MOBILENET_V2_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, SemanticSegmenterOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, MobileNetV2ForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513") >>> model = MobileNetV2ForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513") >>> inputs = image_processor(images=image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" 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 outputs = self.mobilenet_v2( pixel_values, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] logits = self.segmentation_head(encoder_hidden_states[-1]) loss = None if labels is not None: if self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") else: # upsample logits to the images' original size upsampled_logits = nn.functional.interpolate( logits, size=labels.shape[-2:], mode="bilinear", align_corners=False ) loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) loss = loss_fct(upsampled_logits, labels) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SemanticSegmenterOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=None, )

transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py/0

{ "file_path": "transformers/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py", "repo_id": "transformers", "token_count": 15255 }

322

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """Tokenization classes for MPNet.""" import collections import os import unicodedata from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt", } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/mpnet-base": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/mpnet-base": {"do_lower_case": True}, } def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() with open(vocab_file, "r", encoding="utf-8") as reader: tokens = reader.readlines() for index, token in enumerate(tokens): token = token.rstrip("\n") vocab[token] = index return vocab def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens class MPNetTokenizer(PreTrainedTokenizer): """ This tokenizer inherits from [`BertTokenizer`] which contains most of the methods. Users should refer to the superclass for more information regarding methods. Args: vocab_file (`str`): Path to the vocabulary file. do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. do_basic_tokenize (`bool`, *optional*, defaults to `True`): Whether or not to do basic tokenization before WordPiece. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="[UNK]", pad_token="<pad>", mask_token="<mask>", tokenize_chinese_chars=True, strip_accents=None, **kwargs, ): bos_token = AddedToken(bos_token, special=True) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, special=True) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, special=True) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, special=True) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, special=True) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, special=True) if isinstance(pad_token, str) else pad_token # Mask token behave like a normal word, i.e. include the space before it mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token if not os.path.isfile(vocab_file): raise ValueError( f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" " model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" ) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) self.do_basic_tokenize = do_basic_tokenize if do_basic_tokenize: self.basic_tokenizer = BasicTokenizer( do_lower_case=do_lower_case, never_split=never_split, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, ) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token)) super().__init__( do_lower_case=do_lower_case, do_basic_tokenize=do_basic_tokenize, never_split=never_split, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, cls_token=cls_token, pad_token=pad_token, mask_token=mask_token, tokenize_chinese_chars=tokenize_chinese_chars, strip_accents=strip_accents, **kwargs, ) @property def do_lower_case(self): return self.basic_tokenizer.do_lower_case @property def vocab_size(self): return len(self.vocab) def get_vocab(self): # "<mask>" is part of the vocab, but was wrongfully added at a wrong index in the fast saved version vocab = self.added_tokens_encoder.copy() vocab.update(self.vocab) return vocab def _tokenize(self, text): split_tokens = [] if self.do_basic_tokenize: for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): # If the token is part of the never_split set if token in self.basic_tokenizer.never_split: split_tokens.append(token) else: split_tokens += self.wordpiece_tokenizer.tokenize(token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.vocab.get(token, self.vocab.get(self.unk_token)) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.ids_to_tokens.get(index, self.unk_token) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" out_string = " ".join(tokens).replace(" ##", "").strip() return out_string def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A MPNet sequence has the following format: - single sequence: `<s> X </s>` - pair of sequences: `<s> A </s></s> B </s>` Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: list of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False ) -> List[int]: """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` methods. Args: token_ids_0 (`List[int]`): List of ids. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Set to True if the token list is already formatted with special tokens for the model Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of ids. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: index = 0 if os.path.isdir(save_directory): vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) else: vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory with open(vocab_file, "w", encoding="utf-8") as writer: for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." " Please check that the vocabulary is not corrupted!" ) index = token_index writer.write(token + "\n") index += 1 return (vocab_file,) # Copied from transformers.models.bert.tokenization_bert.BasicTokenizer class BasicTokenizer(object): """ Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (`bool`, *optional*, defaults to `True`): Whether or not to lowercase the input when tokenizing. never_split (`Iterable`, *optional*): Collection of tokens which will never be split during tokenization. Only has an effect when `do_basic_tokenize=True` tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)). strip_accents (`bool`, *optional*): Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for `lowercase` (as in the original BERT). do_split_on_punc (`bool`, *optional*, defaults to `True`): In some instances we want to skip the basic punctuation splitting so that later tokenization can capture the full context of the words, such as contractions. """ def __init__( self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None, do_split_on_punc=True, ): if never_split is None: never_split = [] self.do_lower_case = do_lower_case self.never_split = set(never_split) self.tokenize_chinese_chars = tokenize_chinese_chars self.strip_accents = strip_accents self.do_split_on_punc = do_split_on_punc def tokenize(self, text, never_split=None): """ Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. Args: never_split (`List[str]`, *optional*) Kept for backward compatibility purposes. Now implemented directly at the base class level (see [`PreTrainedTokenizer.tokenize`]) List of token not to split. """ # union() returns a new set by concatenating the two sets. never_split = self.never_split.union(set(never_split)) if never_split else self.never_split text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). if self.tokenize_chinese_chars: text = self._tokenize_chinese_chars(text) # prevents treating the same character with different unicode codepoints as different characters unicode_normalized_text = unicodedata.normalize("NFC", text) orig_tokens = whitespace_tokenize(unicode_normalized_text) split_tokens = [] for token in orig_tokens: if token not in never_split: if self.do_lower_case: token = token.lower() if self.strip_accents is not False: token = self._run_strip_accents(token) elif self.strip_accents: token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token, never_split)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text, never_split=None): """Splits punctuation on a piece of text.""" if not self.do_split_on_punc or (never_split is not None and text in never_split): return [text] chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0x4E00 and cp <= 0x9FFF) or (cp >= 0x3400 and cp <= 0x4DBF) # or (cp >= 0x20000 and cp <= 0x2A6DF) # or (cp >= 0x2A700 and cp <= 0x2B73F) # or (cp >= 0x2B740 and cp <= 0x2B81F) # or (cp >= 0x2B820 and cp <= 0x2CEAF) # or (cp >= 0xF900 and cp <= 0xFAFF) or (cp >= 0x2F800 and cp <= 0x2FA1F) # ): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xFFFD or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) # Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer class WordpieceTokenizer(object): """Runs WordPiece tokenization.""" def __init__(self, vocab, unk_token, max_input_chars_per_word=100): self.vocab = vocab self.unk_token = unk_token self.max_input_chars_per_word = max_input_chars_per_word def tokenize(self, text): """ Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example, `input = "unaffable"` wil return as output `["un", "##aff", "##able"]`. Args: text: A single token or whitespace separated tokens. This should have already been passed through *BasicTokenizer*. Returns: A list of wordpiece tokens. """ output_tokens = [] for token in whitespace_tokenize(text): chars = list(token) if len(chars) > self.max_input_chars_per_word: output_tokens.append(self.unk_token) continue is_bad = False start = 0 sub_tokens = [] while start < len(chars): end = len(chars) cur_substr = None while start < end: substr = "".join(chars[start:end]) if start > 0: substr = "##" + substr if substr in self.vocab: cur_substr = substr break end -= 1 if cur_substr is None: is_bad = True break sub_tokens.append(cur_substr) start = end if is_bad: output_tokens.append(self.unk_token) else: output_tokens.extend(sub_tokens) return output_tokens

transformers/src/transformers/models/mpnet/tokenization_mpnet.py/0

{ "file_path": "transformers/src/transformers/models/mpnet/tokenization_mpnet.py", "repo_id": "transformers", "token_count": 9997 }

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# coding=utf-8 # Copyright 2023 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. """Convert MusicGen checkpoints from the original repository.""" import argparse from pathlib import Path from typing import Dict, OrderedDict, Tuple import torch from audiocraft.models import MusicGen from transformers import ( AutoFeatureExtractor, AutoTokenizer, EncodecModel, MusicgenDecoderConfig, MusicgenForConditionalGeneration, MusicgenProcessor, T5EncoderModel, ) from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) EXPECTED_MISSING_KEYS = ["model.decoder.embed_positions.weights"] def rename_keys(name): if "emb" in name: name = name.replace("emb", "model.decoder.embed_tokens") if "transformer" in name: name = name.replace("transformer", "model.decoder") if "cross_attention" in name: name = name.replace("cross_attention", "encoder_attn") if "linear1" in name: name = name.replace("linear1", "fc1") if "linear2" in name: name = name.replace("linear2", "fc2") if "norm1" in name: name = name.replace("norm1", "self_attn_layer_norm") if "norm_cross" in name: name = name.replace("norm_cross", "encoder_attn_layer_norm") if "norm2" in name: name = name.replace("norm2", "final_layer_norm") if "out_norm" in name: name = name.replace("out_norm", "model.decoder.layer_norm") if "linears" in name: name = name.replace("linears", "lm_heads") if "condition_provider.conditioners.description.output_proj" in name: name = name.replace("condition_provider.conditioners.description.output_proj", "enc_to_dec_proj") return name def rename_state_dict(state_dict: OrderedDict, hidden_size: int) -> Tuple[Dict, Dict]: """Function that takes the fairseq Musicgen state dict and renames it according to the HF module names. It further partitions the state dict into the decoder (LM) state dict, and that for the encoder-decoder projection.""" keys = list(state_dict.keys()) enc_dec_proj_state_dict = {} for key in keys: val = state_dict.pop(key) key = rename_keys(key) if "in_proj_weight" in key: # split fused qkv proj state_dict[key.replace("in_proj_weight", "q_proj.weight")] = val[:hidden_size, :] state_dict[key.replace("in_proj_weight", "k_proj.weight")] = val[hidden_size : 2 * hidden_size, :] state_dict[key.replace("in_proj_weight", "v_proj.weight")] = val[-hidden_size:, :] elif "enc_to_dec_proj" in key: enc_dec_proj_state_dict[key[len("enc_to_dec_proj.") :]] = val else: state_dict[key] = val return state_dict, enc_dec_proj_state_dict def decoder_config_from_checkpoint(checkpoint: str) -> MusicgenDecoderConfig: if checkpoint == "small" or checkpoint == "facebook/musicgen-stereo-small": # default config values hidden_size = 1024 num_hidden_layers = 24 num_attention_heads = 16 elif checkpoint == "medium" or checkpoint == "facebook/musicgen-stereo-medium": hidden_size = 1536 num_hidden_layers = 48 num_attention_heads = 24 elif checkpoint == "large" or checkpoint == "facebook/musicgen-stereo-large": hidden_size = 2048 num_hidden_layers = 48 num_attention_heads = 32 else: raise ValueError( "Checkpoint should be one of `['small', 'medium', 'large']` for the mono checkpoints, " "or `['facebook/musicgen-stereo-small', 'facebook/musicgen-stereo-medium', 'facebook/musicgen-stereo-large']` " f"for the stereo checkpoints, got {checkpoint}." ) if "stereo" in checkpoint: audio_channels = 2 num_codebooks = 8 else: audio_channels = 1 num_codebooks = 4 config = MusicgenDecoderConfig( hidden_size=hidden_size, ffn_dim=hidden_size * 4, num_hidden_layers=num_hidden_layers, num_attention_heads=num_attention_heads, num_codebooks=num_codebooks, audio_channels=audio_channels, ) return config @torch.no_grad() def convert_musicgen_checkpoint( checkpoint, pytorch_dump_folder=None, repo_id=None, device="cpu", safe_serialization=False ): fairseq_model = MusicGen.get_pretrained(checkpoint, device=device) decoder_config = decoder_config_from_checkpoint(checkpoint) decoder_state_dict = fairseq_model.lm.state_dict() decoder_state_dict, enc_dec_proj_state_dict = rename_state_dict( decoder_state_dict, hidden_size=decoder_config.hidden_size ) text_encoder = T5EncoderModel.from_pretrained("google-t5/t5-base") audio_encoder = EncodecModel.from_pretrained("facebook/encodec_32khz") decoder = MusicgenForCausalLM(decoder_config).eval() # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection missing_keys, unexpected_keys = decoder.load_state_dict(decoder_state_dict, strict=False) for key in missing_keys.copy(): if key.startswith(("text_encoder", "audio_encoder")) or key in EXPECTED_MISSING_KEYS: missing_keys.remove(key) if len(missing_keys) > 0: raise ValueError(f"Missing key(s) in state_dict: {missing_keys}") if len(unexpected_keys) > 0: raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}") # init the composite model model = MusicgenForConditionalGeneration(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder) # load the pre-trained enc-dec projection (from the decoder state dict) model.enc_to_dec_proj.load_state_dict(enc_dec_proj_state_dict) # check we can do a forward pass input_ids = torch.arange(0, 2 * decoder_config.num_codebooks, dtype=torch.long).reshape(2, -1) decoder_input_ids = input_ids.reshape(2 * decoder_config.num_codebooks, -1) with torch.no_grad(): logits = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids).logits if logits.shape != (2 * decoder_config.num_codebooks, 1, 2048): raise ValueError("Incorrect shape for logits") # now construct the processor tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base") feature_extractor = AutoFeatureExtractor.from_pretrained( "facebook/encodec_32khz", padding_side="left", feature_size=decoder_config.audio_channels ) processor = MusicgenProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) # set the appropriate bos/pad token ids model.generation_config.decoder_start_token_id = 2048 model.generation_config.pad_token_id = 2048 # set other default generation config params model.generation_config.max_length = int(30 * audio_encoder.config.frame_rate) model.generation_config.do_sample = True model.generation_config.guidance_scale = 3.0 if pytorch_dump_folder is not None: Path(pytorch_dump_folder).mkdir(exist_ok=True) logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}") model.save_pretrained(pytorch_dump_folder, safe_serialization=safe_serialization) processor.save_pretrained(pytorch_dump_folder) if repo_id: logger.info(f"Pushing model {checkpoint} to {repo_id}") model.push_to_hub(repo_id, safe_serialization=safe_serialization) processor.push_to_hub(repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint", default="small", type=str, help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: " "`['small', 'medium', 'large']` for the mono checkpoints, or " "`['facebook/musicgen-stereo-small', 'facebook/musicgen-stereo-medium', 'facebook/musicgen-stereo-large']` " "for the stereo checkpoints.", ) parser.add_argument( "--pytorch_dump_folder", required=True, default=None, type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." ) parser.add_argument( "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda." ) parser.add_argument( "--safe_serialization", action="store_true", help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).", ) args = parser.parse_args() convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

transformers/src/transformers/models/musicgen/convert_musicgen_transformers.py/0

{ "file_path": "transformers/src/transformers/models/musicgen/convert_musicgen_transformers.py", "repo_id": "transformers", "token_count": 3651 }

324

# coding=utf-8 # Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch Neighborhood Attention Transformer model.""" import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BackboneOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, OptionalDependencyNotAvailable, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_natten_available, logging, replace_return_docstrings, requires_backends, ) from ...utils.backbone_utils import BackboneMixin from .configuration_nat import NatConfig if is_natten_available(): from natten.functional import natten2dav, natten2dqkrpb else: def natten2dqkrpb(*args, **kwargs): raise OptionalDependencyNotAvailable() def natten2dav(*args, **kwargs): raise OptionalDependencyNotAvailable() logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "NatConfig" # Base docstring _CHECKPOINT_FOR_DOC = "shi-labs/nat-mini-in1k-224" _EXPECTED_OUTPUT_SHAPE = [1, 7, 7, 512] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "shi-labs/nat-mini-in1k-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat" NAT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "shi-labs/nat-mini-in1k-224", # See all Nat models at https://huggingface.co/models?filter=nat ] # drop_path and NatDropPath are from the timm library. @dataclass class NatEncoderOutput(ModelOutput): """ Nat encoder's outputs, with potential hidden states and attentions. Args: 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the 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 stage) 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. reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class NatModelOutput(ModelOutput): """ Nat model's outputs that also contains a pooling of the last hidden states. Args: 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. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): Average pooling of the last layer hidden-state. 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the 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 stage) 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. reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. """ last_hidden_state: torch.FloatTensor = None pooler_output: Optional[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None @dataclass class NatImageClassifierOutput(ModelOutput): """ Nat outputs for image classification. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). 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 + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the 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 stage) 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. reshaped_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 + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None reshaped_hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None class NatEmbeddings(nn.Module): """ Construct the patch and position embeddings. """ def __init__(self, config): super().__init__() self.patch_embeddings = NatPatchEmbeddings(config) self.norm = nn.LayerNorm(config.embed_dim) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, pixel_values: Optional[torch.FloatTensor]) -> Tuple[torch.Tensor]: embeddings = self.patch_embeddings(pixel_values) embeddings = self.norm(embeddings) embeddings = self.dropout(embeddings) return embeddings class NatPatchEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config): super().__init__() patch_size = config.patch_size num_channels, hidden_size = config.num_channels, config.embed_dim self.num_channels = num_channels if patch_size == 4: pass else: # TODO: Support arbitrary patch sizes. raise ValueError("Dinat only supports patch size of 4 at the moment.") self.projection = nn.Sequential( nn.Conv2d(self.num_channels, hidden_size // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), nn.Conv2d(hidden_size // 2, hidden_size, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)), ) def forward(self, pixel_values: Optional[torch.FloatTensor]) -> torch.Tensor: _, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embeddings = self.projection(pixel_values) embeddings = embeddings.permute(0, 2, 3, 1) return embeddings class NatDownsampler(nn.Module): """ Convolutional Downsampling Layer. Args: dim (`int`): Number of input channels. norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`): Normalization layer class. """ def __init__(self, dim: int, norm_layer: nn.Module = nn.LayerNorm) -> None: super().__init__() self.dim = dim self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) self.norm = norm_layer(2 * dim) def forward(self, input_feature: torch.Tensor) -> torch.Tensor: input_feature = self.reduction(input_feature.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) input_feature = self.norm(input_feature) return input_feature # Copied from transformers.models.beit.modeling_beit.drop_path def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: """ Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0.0 or not training: return input keep_prob = 1 - drop_prob shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) random_tensor.floor_() # binarize output = input.div(keep_prob) * random_tensor return output # Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->Nat class NatDropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" def __init__(self, drop_prob: Optional[float] = None) -> None: super().__init__() self.drop_prob = drop_prob def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return drop_path(hidden_states, self.drop_prob, self.training) def extra_repr(self) -> str: return "p={}".format(self.drop_prob) class NeighborhoodAttention(nn.Module): def __init__(self, config, dim, num_heads, kernel_size): super().__init__() if dim % num_heads != 0: raise ValueError( f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})" ) self.num_attention_heads = num_heads self.attention_head_size = int(dim / num_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.kernel_size = kernel_size # rpb is learnable relative positional biases; same concept is used Swin. self.rpb = nn.Parameter(torch.zeros(num_heads, (2 * self.kernel_size - 1), (2 * self.kernel_size - 1))) self.query = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(self.all_head_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 3, 1, 2, 4) def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: query_layer = self.transpose_for_scores(self.query(hidden_states)) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) # Apply the scale factor before computing attention weights. It's usually more efficient because # attention weights are typically a bigger tensor compared to query. # It gives identical results because scalars are commutable in matrix multiplication. query_layer = query_layer / math.sqrt(self.attention_head_size) # Compute NA between "query" and "key" to get the raw attention scores, and add relative positional biases. attention_scores = natten2dqkrpb(query_layer, key_layer, self.rpb, self.kernel_size, 1) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = natten2dav(attention_probs, value_layer, self.kernel_size, 1) context_layer = context_layer.permute(0, 2, 3, 1, 4).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class NeighborhoodAttentionOutput(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(dim, dim) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class NeighborhoodAttentionModule(nn.Module): def __init__(self, config, dim, num_heads, kernel_size): super().__init__() self.self = NeighborhoodAttention(config, dim, num_heads, kernel_size) self.output = NeighborhoodAttentionOutput(config, dim) self.pruned_heads = set() def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: self_outputs = self.self(hidden_states, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class NatIntermediate(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(dim, int(config.mlp_ratio * dim)) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class NatOutput(nn.Module): def __init__(self, config, dim): super().__init__() self.dense = nn.Linear(int(config.mlp_ratio * dim), dim) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class NatLayer(nn.Module): def __init__(self, config, dim, num_heads, drop_path_rate=0.0): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.kernel_size = config.kernel_size self.layernorm_before = nn.LayerNorm(dim, eps=config.layer_norm_eps) self.attention = NeighborhoodAttentionModule(config, dim, num_heads, kernel_size=self.kernel_size) self.drop_path = NatDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() self.layernorm_after = nn.LayerNorm(dim, eps=config.layer_norm_eps) self.intermediate = NatIntermediate(config, dim) self.output = NatOutput(config, dim) self.layer_scale_parameters = ( nn.Parameter(config.layer_scale_init_value * torch.ones((2, dim)), requires_grad=True) if config.layer_scale_init_value > 0 else None ) def maybe_pad(self, hidden_states, height, width): window_size = self.kernel_size pad_values = (0, 0, 0, 0, 0, 0) if height < window_size or width < window_size: pad_l = pad_t = 0 pad_r = max(0, window_size - width) pad_b = max(0, window_size - height) pad_values = (0, 0, pad_l, pad_r, pad_t, pad_b) hidden_states = nn.functional.pad(hidden_states, pad_values) return hidden_states, pad_values def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, torch.Tensor]: batch_size, height, width, channels = hidden_states.size() shortcut = hidden_states hidden_states = self.layernorm_before(hidden_states) # pad hidden_states if they are smaller than kernel size hidden_states, pad_values = self.maybe_pad(hidden_states, height, width) _, height_pad, width_pad, _ = hidden_states.shape attention_outputs = self.attention(hidden_states, output_attentions=output_attentions) attention_output = attention_outputs[0] was_padded = pad_values[3] > 0 or pad_values[5] > 0 if was_padded: attention_output = attention_output[:, :height, :width, :].contiguous() if self.layer_scale_parameters is not None: attention_output = self.layer_scale_parameters[0] * attention_output hidden_states = shortcut + self.drop_path(attention_output) layer_output = self.layernorm_after(hidden_states) layer_output = self.output(self.intermediate(layer_output)) if self.layer_scale_parameters is not None: layer_output = self.layer_scale_parameters[1] * layer_output layer_output = hidden_states + self.drop_path(layer_output) layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,) return layer_outputs class NatStage(nn.Module): def __init__(self, config, dim, depth, num_heads, drop_path_rate, downsample): super().__init__() self.config = config self.dim = dim self.layers = nn.ModuleList( [ NatLayer( config=config, dim=dim, num_heads=num_heads, drop_path_rate=drop_path_rate[i], ) for i in range(depth) ] ) # patch merging layer if downsample is not None: self.downsample = downsample(dim=dim, norm_layer=nn.LayerNorm) else: self.downsample = None self.pointing = False def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor]: _, height, width, _ = hidden_states.size() for i, layer_module in enumerate(self.layers): layer_outputs = layer_module(hidden_states, output_attentions) hidden_states = layer_outputs[0] hidden_states_before_downsampling = hidden_states if self.downsample is not None: hidden_states = self.downsample(hidden_states_before_downsampling) stage_outputs = (hidden_states, hidden_states_before_downsampling) if output_attentions: stage_outputs += layer_outputs[1:] return stage_outputs class NatEncoder(nn.Module): def __init__(self, config): super().__init__() self.num_levels = len(config.depths) self.config = config dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))] self.levels = nn.ModuleList( [ NatStage( config=config, dim=int(config.embed_dim * 2**i_layer), depth=config.depths[i_layer], num_heads=config.num_heads[i_layer], drop_path_rate=dpr[sum(config.depths[:i_layer]) : sum(config.depths[: i_layer + 1])], downsample=NatDownsampler if (i_layer < self.num_levels - 1) else None, ) for i_layer in range(self.num_levels) ] ) def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, output_hidden_states: Optional[bool] = False, output_hidden_states_before_downsampling: Optional[bool] = False, return_dict: Optional[bool] = True, ) -> Union[Tuple, NatEncoderOutput]: all_hidden_states = () if output_hidden_states else None all_reshaped_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None if output_hidden_states: # rearrange b h w c -> b c h w reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2) all_hidden_states += (hidden_states,) all_reshaped_hidden_states += (reshaped_hidden_state,) for i, layer_module in enumerate(self.levels): layer_outputs = layer_module(hidden_states, output_attentions) hidden_states = layer_outputs[0] hidden_states_before_downsampling = layer_outputs[1] if output_hidden_states and output_hidden_states_before_downsampling: # rearrange b h w c -> b c h w reshaped_hidden_state = hidden_states_before_downsampling.permute(0, 3, 1, 2) all_hidden_states += (hidden_states_before_downsampling,) all_reshaped_hidden_states += (reshaped_hidden_state,) elif output_hidden_states and not output_hidden_states_before_downsampling: # rearrange b h w c -> b c h w reshaped_hidden_state = hidden_states.permute(0, 3, 1, 2) all_hidden_states += (hidden_states,) all_reshaped_hidden_states += (reshaped_hidden_state,) if output_attentions: all_self_attentions += layer_outputs[2:] if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return NatEncoderOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, reshaped_hidden_states=all_reshaped_hidden_states, ) class NatPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = NatConfig base_model_prefix = "nat" main_input_name = "pixel_values" def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) NAT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`NatConfig`]): 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. """ NAT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ViTImageProcessor.__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. """ @add_start_docstrings( "The bare Nat Model transformer outputting raw hidden-states without any specific head on top.", NAT_START_DOCSTRING, ) class NatModel(NatPreTrainedModel): def __init__(self, config, add_pooling_layer=True): super().__init__(config) requires_backends(self, ["natten"]) self.config = config self.num_levels = len(config.depths) self.num_features = int(config.embed_dim * 2 ** (self.num_levels - 1)) self.embeddings = NatEmbeddings(config) self.encoder = NatEncoder(config) self.layernorm = nn.LayerNorm(self.num_features, eps=config.layer_norm_eps) self.pooler = nn.AdaptiveAvgPool1d(1) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=NatModelOutput, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, NatModelOutput]: 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 pixel_values is None: raise ValueError("You have to specify pixel_values") embedding_output = self.embeddings(pixel_values) encoder_outputs = self.encoder( embedding_output, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = None if self.pooler is not None: pooled_output = self.pooler(sequence_output.flatten(1, 2).transpose(1, 2)) pooled_output = torch.flatten(pooled_output, 1) if not return_dict: output = (sequence_output, pooled_output) + encoder_outputs[1:] return output return NatModelOutput( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, reshaped_hidden_states=encoder_outputs.reshaped_hidden_states, ) @add_start_docstrings( """ Nat Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. """, NAT_START_DOCSTRING, ) class NatForImageClassification(NatPreTrainedModel): def __init__(self, config): super().__init__(config) requires_backends(self, ["natten"]) self.num_labels = config.num_labels self.nat = NatModel(config) # Classifier head self.classifier = ( nn.Linear(self.nat.num_features, config.num_labels) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=NatImageClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, NatImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image 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 outputs = self.nat( pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return NatImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, reshaped_hidden_states=outputs.reshaped_hidden_states, ) @add_start_docstrings( "NAT backbone, to be used with frameworks like DETR and MaskFormer.", NAT_START_DOCSTRING, ) class NatBackbone(NatPreTrainedModel, BackboneMixin): def __init__(self, config): super().__init__(config) super()._init_backbone(config) requires_backends(self, ["natten"]) self.embeddings = NatEmbeddings(config) self.encoder = NatEncoder(config) self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))] # Add layer norms to hidden states of out_features hidden_states_norms = {} for stage, num_channels in zip(self.out_features, self.channels): hidden_states_norms[stage] = nn.LayerNorm(num_channels) self.hidden_states_norms = nn.ModuleDict(hidden_states_norms) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings @add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.Tensor, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> BackboneOutput: """ Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224") >>> model = AutoBackbone.from_pretrained( ... "shi-labs/nat-mini-in1k-224", out_features=["stage1", "stage2", "stage3", "stage4"] ... ) >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) >>> feature_maps = outputs.feature_maps >>> list(feature_maps[-1].shape) [1, 512, 7, 7] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions embedding_output = self.embeddings(pixel_values) outputs = self.encoder( embedding_output, output_attentions=output_attentions, output_hidden_states=True, output_hidden_states_before_downsampling=True, return_dict=True, ) hidden_states = outputs.reshaped_hidden_states feature_maps = () for stage, hidden_state in zip(self.stage_names, hidden_states): if stage in self.out_features: # TODO can we simplify this? batch_size, num_channels, height, width = hidden_state.shape hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous() hidden_state = hidden_state.view(batch_size, height * width, num_channels) hidden_state = self.hidden_states_norms[stage](hidden_state) hidden_state = hidden_state.view(batch_size, height, width, num_channels) hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() feature_maps += (hidden_state,) if not return_dict: output = (feature_maps,) if output_hidden_states: output += (outputs.hidden_states,) return output return BackboneOutput( feature_maps=feature_maps, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, )

transformers/src/transformers/models/nat/modeling_nat.py/0

{ "file_path": "transformers/src/transformers/models/nat/modeling_nat.py", "repo_id": "transformers", "token_count": 16471 }

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# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization classes for OpenAI GPT.""" from typing import Optional, Tuple from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_openai import OpenAIGPTTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "openai-community/openai-gpt": "https://huggingface.co/openai-community/openai-gpt/resolve/main/vocab.json" }, "merges_file": { "openai-community/openai-gpt": "https://huggingface.co/openai-community/openai-gpt/resolve/main/merges.txt" }, "tokenizer_file": { "openai-community/openai-gpt": "https://huggingface.co/openai-community/openai-gpt/resolve/main/tokenizer.json" }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "openai-community/openai-gpt": 512, } class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast): """ Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with the following peculiarities: - lower case all inputs - uses BERT's BasicTokenizer for pre-BPE tokenization This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] slow_tokenizer_class = OpenAIGPTTokenizer def __init__(self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<unk>", **kwargs): super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, **kwargs) @property def do_lower_case(self): return True def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files)

transformers/src/transformers/models/openai/tokenization_openai_fast.py/0

{ "file_path": "transformers/src/transformers/models/openai/tokenization_openai_fast.py", "repo_id": "transformers", "token_count": 1175 }

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# coding=utf-8 # Copyright 2020 Google and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging SPIECE_UNDERLINE = "▁" VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"} } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "google/pegasus-xsum": 512, } logger = logging.get_logger(__name__) # TODO ArthurZ refactor this to only use the added_tokens_encoder class PegasusTokenizer(PreTrainedTokenizer): r""" Construct a PEGASUS tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. mask_token (`str`, *optional*, defaults to `"<mask_2>"`): The token used for masking single token values. This is the token used when training this model with masked language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining. It corresponds to *[MASK2]* in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf). mask_token_sent (`str`, *optional*, defaults to `"<mask_1>"`): The token used for masking whole target sentences. This is the token used when training this model with gap sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during pretraining. It corresponds to *[MASK1]* in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf). additional_special_tokens (`List[str]`, *optional*): Additional special tokens used by the tokenizer. If no additional_special_tokens are provided <mask_2> and <unk_2, ..., unk_102> are used as additional special tokens corresponding to the [original PEGASUS tokenizer](https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66) that uses the tokens 2 - 104 only for pretraining sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, pad_token="<pad>", eos_token="</s>", unk_token="<unk>", mask_token="<mask_2>", mask_token_sent="<mask_1>", additional_special_tokens=None, offset=103, # entries 2 - 104 are only used for pretraining sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs, ) -> None: self.offset = offset if additional_special_tokens is not None: if not isinstance(additional_special_tokens, list): raise TypeError( f"additional_special_tokens should be of type {type(list)}, but is" f" {type(additional_special_tokens)}" ) additional_special_tokens_extended = ( ([mask_token_sent] + additional_special_tokens) if mask_token_sent not in additional_special_tokens and mask_token_sent is not None else additional_special_tokens ) # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken additional_special_tokens_extended += [ f"<unk_{i}>" for i in range(len(additional_special_tokens_extended), self.offset - 1) ] if len(set(additional_special_tokens_extended)) != len(additional_special_tokens_extended): raise ValueError( "Please make sure that the provided additional_special_tokens do not contain an incorrectly" f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}." ) additional_special_tokens = additional_special_tokens_extended else: additional_special_tokens_extended = [] additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else [] additional_special_tokens += [f"<unk_{i}>" for i in range(2, self.offset)] self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.mask_token_sent = mask_token_sent self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) _added_tokens_decoder = { 0: AddedToken(str(pad_token), special=True), 1: AddedToken(str(eos_token), special=True), } if self.mask_token_sent is not None: _added_tokens_decoder[2] = AddedToken(mask_token_sent, special=True) _added_tokens_decoder[3] = AddedToken(str(mask_token), special=True) for i in range(2, self.offset): _added_tokens_decoder[len(_added_tokens_decoder)] = AddedToken(f"<unk_{i}>", special=True) # Force update as we want to make sure vocab is enforced (same as fast) self._added_tokens_decoder = kwargs.pop("added_tokens_decoder", {}) self._added_tokens_decoder.update(_added_tokens_decoder) super().__init__( eos_token=eos_token, unk_token=unk_token, mask_token=mask_token, pad_token=pad_token, mask_token_sent=mask_token_sent, offset=offset, additional_special_tokens=additional_special_tokens, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) @property def vocab_size(self) -> int: return len(self.sp_model) + self.offset def get_vocab(self) -> Dict[str, int]: vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) to an id using the vocab.""" sp_id = self.sp_model.piece_to_id(token) return sp_id + self.offset def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) to a token (str) using the vocab.""" if index < self.offset: return self.sp_model.IdToPiece(index) token = self.sp_model.IdToPiece(index - self.offset) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode(current_sub_tokens) + token current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode(current_sub_tokens) return out_string.strip() def num_special_tokens_to_add(self, pair=False): """Just EOS""" return 1 def _special_token_mask(self, seq): all_special_ids = set(self.all_special_ids) # call it once instead of inside list comp all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special return [1 if x in all_special_ids else 0 for x in seq] def get_special_tokens_mask( self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False ) -> List[int]: """Get list where entries are [1] if a token is [eos] or [pad] else 0.""" if already_has_special_tokens: return self._special_token_mask(token_ids_0) elif token_ids_1 is None: return self._special_token_mask(token_ids_0) + [1] else: return self._special_token_mask(token_ids_0 + token_ids_1) + [1] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]: """ Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence: - single sequence: `X </s>` - pair of sequences: `A B </s>` (not intended use) BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator. Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return token_ids_0 + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return token_ids_0 + token_ids_1 + [self.eos_token_id] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,)

transformers/src/transformers/models/pegasus/tokenization_pegasus.py/0

{ "file_path": "transformers/src/transformers/models/pegasus/tokenization_pegasus.py", "repo_id": "transformers", "token_count": 5716 }

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# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ProphetNet checkpoint.""" import argparse from torch import nn # transformers_old should correspond to branch `save_old_prophetnet_model_structure` here # original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively from transformers_old.modeling_prophetnet import ( ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld, ) from transformers_old.modeling_xlm_prophetnet import ( XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld, ) from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging logger = logging.get_logger(__name__) logging.set_verbosity_info() def convert_prophetnet_checkpoint_to_pytorch(prophetnet_checkpoint_path: str, pytorch_dump_folder_path: str): """ Copy/paste/tweak prohpetnet's weights to our prophetnet structure. """ if "xprophetnet" in prophetnet_checkpoint_path: prophet_old = XLMProphetNetForConditionalGenerationOld.from_pretrained(prophetnet_checkpoint_path) prophet, loading_info = XLMProphetNetForConditionalGeneration.from_pretrained( prophetnet_checkpoint_path, output_loading_info=True ) else: prophet_old = ProphetNetForConditionalGenerationOld.from_pretrained(prophetnet_checkpoint_path) prophet, loading_info = ProphetNetForConditionalGeneration.from_pretrained( prophetnet_checkpoint_path, output_loading_info=True ) special_keys = ["key_proj", "value_proj", "query_proj"] mapping = { "self_attn": "ngram_self_attn", "cross_attn": "encoder_attn", "cross_attn_layer_norm": "encoder_attn_layer_norm", "feed_forward_layer_norm": "final_layer_norm", "feed_forward": "", "intermediate": "fc1", "output": "fc2", "key_proj": "k_proj", "query_proj": "q_proj", "value_proj": "v_proj", "word_embeddings": "embed_tokens", "embeddings_layer_norm": "emb_layer_norm", "relative_pos_embeddings": "relative_linear", "ngram_embeddings": "ngram_input_embed", "position_embeddings": "embed_positions", } for key in loading_info["missing_keys"]: attributes = key.split(".") if attributes[0] == "lm_head": model = prophet old_model = prophet_old else: model = prophet.prophetnet old_model = prophet_old.model is_key_init = False for attribute in attributes: if attribute in mapping: old_attribute = mapping[attribute] if not hasattr(old_model, old_attribute) and len(old_attribute) > 0: old_attribute = attribute elif hasattr(old_model, attribute): old_attribute = attribute if attribute == "weight": assert old_model.weight.shape == model.weight.shape, "Shapes have to match!" model.weight = old_model.weight logger.info(f"{attribute} is initialized.") is_key_init = True break elif attribute == "bias": assert old_model.bias.shape == model.bias.shape, "Shapes have to match!" model.bias = old_model.bias logger.info(f"{attribute} is initialized") is_key_init = True break elif attribute in special_keys and hasattr(old_model, "in_proj_weight"): embed_dim = old_model.in_proj_weight.shape[0] // 3 param = getattr(model, attribute) param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match" param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match" if attribute == "query_proj": model.query_proj.weight = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]) model.query_proj.bias = nn.Parameter(old_model.in_proj_bias[:embed_dim]) elif attribute == "key_proj": model.key_proj.weight = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]) model.key_proj.bias = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]) elif attribute == "value_proj": model.value_proj.weight = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]) model.value_proj.bias = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]) is_key_init = True break elif attribute == "position_embeddings": assert ( model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1] ), "Hidden size has to match" assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings." model.position_embeddings.weight = nn.Parameter(old_model.embed_positions.weight[:512, :]) is_key_init = True break if attribute.isdigit(): model = model[int(attribute)] old_model = old_model[int(old_attribute)] else: model = getattr(model, attribute) if old_attribute == "": old_model = old_model else: if not hasattr(old_model, old_attribute): raise ValueError(f"{old_model} does not have {old_attribute}") old_model = getattr(old_model, old_attribute) if not is_key_init: raise ValueError(f"{key} was not correctly initialized!") print(f"Saving model to {pytorch_dump_folder_path}") prophet.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) args = parser.parse_args() convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

transformers/src/transformers/models/prophetnet/convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py/0

{ "file_path": "transformers/src/transformers/models/prophetnet/convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py", "repo_id": "transformers", "token_count": 3107 }

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# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group 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. """ Qwen2 model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) QWEN2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "Qwen/Qwen2-7B-beta": "https://huggingface.co/Qwen/Qwen2-7B-beta/resolve/main/config.json", } class Qwen2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta). 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 151936): Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Qwen2Model`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 32): 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 `32`. 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 32768): The maximum sequence length that this model might ever be used with. 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-06): The epsilon used by the rms normalization layers. 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`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 4096): Sliding window attention (SWA) window size. If not specified, will default to `4096`. max_window_layers (`int`, *optional*, defaults to 28): The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import Qwen2Model, Qwen2Config >>> # Initializing a Qwen2 style configuration >>> configuration = Qwen2Config() >>> # Initializing a model from the Qwen2-7B style configuration >>> model = Qwen2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=151936, hidden_size=4096, intermediate_size=22016, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=28, attention_dropout=0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings 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.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout super().__init__( tie_word_embeddings=tie_word_embeddings, **kwargs, )

transformers/src/transformers/models/qwen2/configuration_qwen2.py/0

{ "file_path": "transformers/src/transformers/models/qwen2/configuration_qwen2.py", "repo_id": "transformers", "token_count": 2536 }

329

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch RoFormer model.""" import math import os from typing import Optional, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel, SequenceSummary from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_roformer import RoFormerConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "junnyu/roformer_chinese_base" _CONFIG_FOR_DOC = "RoFormerConfig" ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [ "junnyu/roformer_chinese_small", "junnyu/roformer_chinese_base", "junnyu/roformer_chinese_char_small", "junnyu/roformer_chinese_char_base", "junnyu/roformer_small_discriminator", "junnyu/roformer_small_generator", # See all RoFormer models at https://huggingface.co/models?filter=roformer ] # Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->RoFormer class RoFormerSinusoidalPositionalEmbedding(nn.Embedding): """This module produces sinusoidal positional embeddings of any length.""" def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None: super().__init__(num_positions, embedding_dim) self.weight = self._init_weight(self.weight) @staticmethod def _init_weight(out: nn.Parameter) -> nn.Parameter: """ Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in the 2nd half of the vector. [dim // 2:] """ n_pos, dim = out.shape position_enc = np.array( [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)] ) out.requires_grad = False # set early to avoid an error in pytorch-1.8+ sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1 out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) out.detach_() return out @torch.no_grad() def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor: """`input_ids_shape` is expected to be [bsz x seqlen].""" bsz, seq_len = input_ids_shape[:2] positions = torch.arange( past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device ) return super().forward(positions) def load_tf_weights_in_roformer(model, config, tf_checkpoint_path): """Load tf checkpoints in a pytorch model.""" try: import re import numpy as np import tensorflow as tf except ImportError: logger.error( "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see " "https://www.tensorflow.org/install/ for installation instructions." ) raise tf_path = os.path.abspath(tf_checkpoint_path) logger.info(f"Converting TensorFlow checkpoint from {tf_path}") # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, shape in init_vars: logger.info(f"Loading TF weight {name} with shape {shape}") array = tf.train.load_variable(tf_path, name) names.append(name.replace("bert", "roformer")) arrays.append(array) for name, array in zip(names, arrays): name = name.split("/") # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v # which are not required for using pretrained model if any( n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"] for n in name ): logger.info(f"Skipping {'/'.join(name)}") continue pointer = model for m_name in name: if re.fullmatch(r"[A-Za-z]+_\d+", m_name): scope_names = re.split(r"_(\d+)", m_name) else: scope_names = [m_name] if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight") elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias") elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight") elif scope_names[0] == "squad": pointer = getattr(pointer, "classifier") else: try: pointer = getattr(pointer, scope_names[0]) except AttributeError: logger.info(f"Skipping {'/'.join(name)}") continue if len(scope_names) >= 2: num = int(scope_names[1]) pointer = pointer[num] if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight") elif m_name == "kernel": array = np.transpose(array) try: if not pointer.shape == array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched") except AssertionError as e: e.args += (pointer.shape, array.shape) raise logger.info(f"Initialize PyTorch weight {name}") pointer.data = torch.from_numpy(array) return model class RoFormerEmbeddings(nn.Module): """Construct the embeddings from word and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None): if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class RoFormerSelfAttention(nn.Module): def __init__(self, config): super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.is_decoder = config.is_decoder self.rotary_value = config.rotary_value def transpose_for_scores(self, x): new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): mixed_query_layer = self.query(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. is_cross_attention = encoder_hidden_states is not None if is_cross_attention and past_key_value is not None: # reuse k,v, cross_attentions key_layer = past_key_value[0] value_layer = past_key_value[1] attention_mask = encoder_attention_mask elif is_cross_attention: key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) attention_mask = encoder_attention_mask else: key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) if sinusoidal_pos is not None: if self.rotary_value: query_layer, key_layer, value_layer = self.apply_rotary_position_embeddings( sinusoidal_pos, query_layer, key_layer, value_layer ) else: query_layer, key_layer = self.apply_rotary_position_embeddings( sinusoidal_pos, query_layer, key_layer ) if past_key_value is not None: key_layer = torch.cat([past_key_value[0], key_layer], dim=2) value_layer = torch.cat([past_key_value[1], value_layer], dim=2) if self.is_decoder: # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. # Further calls to cross_attention layer can then reuse all cross-attention # key/value_states (first "if" case) # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of # all previous decoder key/value_states. Further calls to uni-directional self-attention # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) # if encoder bi-directional self-attention `past_key_value` is always `None` past_key_value = (key_layer, value_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in RoFormerModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) if self.is_decoder: outputs = outputs + (past_key_value,) return outputs @staticmethod def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None): # https://kexue.fm/archives/8265 # sin [batch_size, num_heads, sequence_length, embed_size_per_head//2] # cos [batch_size, num_heads, sequence_length, embed_size_per_head//2] sin, cos = sinusoidal_pos.chunk(2, dim=-1) # sin [θ0,θ1,θ2......θd/2-1] -> sin_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos) # cos [θ0,θ1,θ2......θd/2-1] -> cos_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos) # rotate_half_query_layer [-q1,q0,-q3,q2......,-qd-1,qd-2] rotate_half_query_layer = torch.stack([-query_layer[..., 1::2], query_layer[..., ::2]], dim=-1).reshape_as( query_layer ) query_layer = query_layer * cos_pos + rotate_half_query_layer * sin_pos # rotate_half_key_layer [-k1,k0,-k3,k2......,-kd-1,kd-2] rotate_half_key_layer = torch.stack([-key_layer[..., 1::2], key_layer[..., ::2]], dim=-1).reshape_as(key_layer) key_layer = key_layer * cos_pos + rotate_half_key_layer * sin_pos if value_layer is not None: # rotate_half_value_layer [-v1,v0,-v3,v2......,-vd-1,vd-2] rotate_half_value_layer = torch.stack([-value_layer[..., 1::2], value_layer[..., ::2]], dim=-1).reshape_as( value_layer ) value_layer = value_layer * cos_pos + rotate_half_value_layer * sin_pos return query_layer, key_layer, value_layer return query_layer, key_layer # Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RoFormer class RoFormerSelfOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class RoFormerAttention(nn.Module): def __init__(self, config): super().__init__() self.self = RoFormerSelfAttention(config) self.output = RoFormerSelfOutput(config) self.pruned_heads = set() # Copied from transformers.models.bert.modeling_bert.BertAttention.prune_heads def prune_heads(self, heads): if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads ) # Prune linear layers self.self.query = prune_linear_layer(self.self.query, index) self.self.key = prune_linear_layer(self.self.key, index) self.self.value = prune_linear_layer(self.self.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.self.num_attention_heads = self.self.num_attention_heads - len(heads) self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) # End Copy def forward( self, hidden_states, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): self_outputs = self.self( hidden_states, attention_mask, sinusoidal_pos, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoFormer class RoFormerIntermediate(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer class RoFormerOutput(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class RoFormerLayer(nn.Module): def __init__(self, config): super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = RoFormerAttention(config) self.is_decoder = config.is_decoder self.add_cross_attention = config.add_cross_attention if self.add_cross_attention: if not self.is_decoder: raise ValueError(f"{self} should be used as a decoder model if cross attention is added") self.crossattention = RoFormerAttention(config) self.intermediate = RoFormerIntermediate(config) self.output = RoFormerOutput(config) def forward( self, hidden_states, attention_mask=None, sinusoidal_pos=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, ): # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attention_outputs = self.attention( hidden_states, attention_mask, sinusoidal_pos, head_mask, output_attentions=output_attentions, past_key_value=self_attn_past_key_value, ) attention_output = self_attention_outputs[0] # if decoder, the last output is tuple of self-attn cache if self.is_decoder: outputs = self_attention_outputs[1:-1] present_key_value = self_attention_outputs[-1] else: outputs = self_attention_outputs[1:] # add self attentions if we output attention weights cross_attn_present_key_value = None if self.is_decoder and encoder_hidden_states is not None: if not hasattr(self, "crossattention"): raise ValueError( f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention " "layers by setting `config.add_cross_attention=True`" ) # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None cross_attention_outputs = self.crossattention( attention_output, attention_mask, sinusoidal_pos, head_mask, encoder_hidden_states, encoder_attention_mask, cross_attn_past_key_value, output_attentions, ) attention_output = cross_attention_outputs[0] outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights # add cross-attn cache to positions 3,4 of present_key_value tuple cross_attn_present_key_value = cross_attention_outputs[-1] present_key_value = present_key_value + cross_attn_present_key_value layer_output = apply_chunking_to_forward( self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output ) outputs = (layer_output,) + outputs # if decoder, return the attn key/values as the last output if self.is_decoder: outputs = outputs + (present_key_value,) return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class RoFormerEncoder(nn.Module): def __init__(self, config): super().__init__() self.config = config self.embed_positions = RoFormerSinusoidalPositionalEmbedding( config.max_position_embeddings, config.hidden_size // config.num_attention_heads ) self.layer = nn.ModuleList([RoFormerLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_values=None, use_cache=None, output_attentions=False, output_hidden_states=False, return_dict=True, ): 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 all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 # [sequence_length, embed_size_per_head] -> [batch_size, num_heads, sequence_length, embed_size_per_head] sinusoidal_pos = self.embed_positions(hidden_states.shape[:-1], past_key_values_length)[None, None, :, :] next_decoder_cache = () if use_cache else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None past_key_value = past_key_values[i] if past_key_values is not None else None if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( layer_module.__call__, hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) else: layer_outputs = layer_module( hidden_states, attention_mask, sinusoidal_pos, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [ hidden_states, next_decoder_cache, all_hidden_states, all_self_attentions, all_cross_attentions, ] if v is not None ) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) class RoFormerPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.embedding_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class RoFormerLMPredictionHead(nn.Module): def __init__(self, config): super().__init__() self.transform = RoFormerPredictionHeadTransform(config) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states # Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RoFormer class RoFormerOnlyMLMHead(nn.Module): def __init__(self, config): super().__init__() self.predictions = RoFormerLMPredictionHead(config) def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: prediction_scores = self.predictions(sequence_output) return prediction_scores class RoFormerPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = RoFormerConfig load_tf_weights = load_tf_weights_in_roformer base_model_prefix = "roformer" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, RoFormerSinusoidalPositionalEmbedding): pass elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) ROFORMER_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`RoFormerConfig`]): 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. """ ROFORMER_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `({0})`, *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) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert *input_ids* indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare RoFormer Model transformer outputting raw hidden-states without any specific head on top.", ROFORMER_START_DOCSTRING, ) class RoFormerModel(RoFormerPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ def __init__(self, config): super().__init__(config) self.config = config self.embeddings = RoFormerEmbeddings(config) if config.embedding_size != config.hidden_size: self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size) self.encoder = RoFormerEncoder(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPastAndCrossAttentions, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[BaseModelOutputWithPastAndCrossAttentions, Tuple[torch.Tensor]]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. 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 = 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 self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) if hasattr(self, "embeddings_project"): embedding_output = self.embeddings_project(embedding_output) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=sequence_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) @add_start_docstrings("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING) class RoFormerForMaskedLM(RoFormerPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) if config.is_decoder: logger.warning( "If you want to use `RoFormerForMaskedLM` make sure `config.is_decoder=False` for " "bi-directional self-attention." ) self.roformer = RoFormerModel(config) self.cls = RoFormerOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MaskedLMOutput, Tuple[torch.Tensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (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]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs): input_shape = input_ids.shape effective_batch_size = input_shape[0] # add a dummy token assert self.config.pad_token_id is not None, "The PAD token should be defined for generation" attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1) dummy_token = torch.full( (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device ) input_ids = torch.cat([input_ids, dummy_token], dim=1) return {"input_ids": input_ids, "attention_mask": attention_mask} @add_start_docstrings( """RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING ) class RoFormerForCausalLM(RoFormerPreTrainedModel): _tied_weights_keys = ["cls.predictions.decoder.bias", "cls.predictions.decoder.weight"] def __init__(self, config): super().__init__(config) if not config.is_decoder: logger.warning("If you want to use `RoFormerForCausalLM` as a standalone, add `is_decoder=True.`") self.roformer = RoFormerModel(config) self.cls = RoFormerOnlyMLMHead(config) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.cls.predictions.decoder def set_output_embeddings(self, new_embeddings): self.cls.predictions.decoder = new_embeddings @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[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[CausalLMOutputWithCrossAttentions, Tuple[torch.Tensor]]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. 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`). Returns: Example: ```python >>> from transformers import AutoTokenizer, RoFormerForCausalLM, RoFormerConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("junnyu/roformer_chinese_base") >>> config = RoFormerConfig.from_pretrained("junnyu/roformer_chinese_base") >>> config.is_decoder = True >>> model = RoFormerForCausalLM.from_pretrained("junnyu/roformer_chinese_base", config=config) >>> inputs = tokenizer("今天天气非常好。", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[1:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **model_kwargs): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past_key_values is used if past_key_values is not None: past_length = past_key_values[0][0].shape[2] # Some generation methods already pass only the last input ID if input_ids.shape[1] > past_length: remove_prefix_length = past_length else: # Default to old behavior: keep only final ID remove_prefix_length = input_ids.shape[1] - 1 input_ids = input_ids[:, remove_prefix_length:] return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past_key_values} def _reorder_cache(self, past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) + layer_past[2:], ) return reordered_past class RoFormerClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.out_proj = nn.Linear(config.hidden_size, config.num_labels) self.config = config def forward(self, features, **kwargs): x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = ACT2FN[self.config.hidden_act](x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ROFORMER_START_DOCSTRING, ) class RoFormerForSequenceClassification(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roformer = RoFormerModel(config) self.classifier = RoFormerClassificationHead(config) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[SequenceClassifierOutput, Tuple[torch.Tensor]]: 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 outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, ROFORMER_START_DOCSTRING, ) class RoFormerForMultipleChoice(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.roformer = RoFormerModel(config) self.sequence_summary = SequenceSummary(config) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward( ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[MultipleChoiceModelOutput, Tuple[torch.Tensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] pooled_output = self.sequence_summary(sequence_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model 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. """, ROFORMER_START_DOCSTRING, ) class RoFormerForTokenClassification(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.roformer = RoFormerModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) 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(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @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, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[TokenClassifierOutput, Tuple[torch.Tensor]]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ RoFormer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ROFORMER_START_DOCSTRING, ) class RoFormerForQuestionAnswering(RoFormerPreTrainedModel): def __init__(self, config): super().__init__(config) config.num_labels = 2 self.num_labels = config.num_labels self.roformer = RoFormerModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[QuestionAnsweringModelOutput, Tuple[torch.Tensor]]: r""" start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.roformer( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) total_loss = None if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions = start_positions.clamp(0, ignored_index) end_positions = end_positions.clamp(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 if not return_dict: output = (start_logits, end_logits) + outputs[1:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

transformers/src/transformers/models/roformer/modeling_roformer.py/0

{ "file_path": "transformers/src/transformers/models/roformer/modeling_roformer.py", "repo_id": "transformers", "token_count": 29911 }

330

# coding=utf-8 # Copyright 2022 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. """Image processor class for Segformer.""" import warnings from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import resize, to_channel_dimension_format from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_kwargs, validate_preprocess_arguments, ) from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging if is_vision_available(): import PIL.Image if is_torch_available(): import torch logger = logging.get_logger(__name__) class SegformerImageProcessor(BaseImageProcessor): r""" Constructs a Segformer image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `(size["height"], size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`): Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. do_reduce_labels (`bool`, *optional*, defaults to `False`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. Can be overridden by the `do_reduce_labels` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BILINEAR, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_reduce_labels: bool = False, **kwargs, ) -> None: if "reduce_labels" in kwargs: warnings.warn( "The `reduce_labels` parameter is deprecated and will be removed in a future version. Please use " "`do_reduce_labels` instead.", FutureWarning, ) do_reduce_labels = kwargs.pop("reduce_labels") super().__init__(**kwargs) size = size if size is not None else {"height": 512, "width": 512} size = get_size_dict(size) self.do_resize = do_resize self.size = size self.resample = resample self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD self.do_reduce_labels = do_reduce_labels self._valid_processor_keys = [ "images", "segmentation_maps", "do_resize", "size", "resample", "do_rescale", "rescale_factor", "do_normalize", "image_mean", "image_std", "do_reduce_labels", "return_tensors", "data_format", "input_data_format", ] @classmethod def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs): """ Overrides the `from_dict` method from the base class to make sure `do_reduce_labels` is updated if image processor is created using from_dict and kwargs e.g. `SegformerImageProcessor.from_pretrained(checkpoint, reduce_labels=True)` """ image_processor_dict = image_processor_dict.copy() if "reduce_labels" in kwargs: image_processor_dict["reduce_labels"] = kwargs.pop("reduce_labels") return super().from_dict(image_processor_dict, **kwargs) # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BILINEAR, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> np.ndarray: """ Resize an image to `(size["height"], size["width"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`. data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Returns: `np.ndarray`: The resized image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}") output_size = (size["height"], size["width"]) return resize( image, size=output_size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs, ) # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label def reduce_label(self, label: ImageInput) -> np.ndarray: label = to_numpy_array(label) # Avoid using underflow conversion label[label == 0] = 255 label = label - 1 label[label == 254] = 255 return label def _preprocess( self, image: ImageInput, do_reduce_labels: bool, do_resize: bool, do_rescale: bool, do_normalize: bool, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, rescale_factor: Optional[float] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ): if do_reduce_labels: image = self.reduce_label(image) if do_resize: image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format) if do_rescale: image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format) if do_normalize: image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format) return image def _preprocess_image( self, image: ImageInput, do_resize: bool = None, size: Dict[str, int] = None, resample: PILImageResampling = None, do_rescale: bool = None, rescale_factor: float = None, do_normalize: bool = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, data_format: Optional[Union[str, ChannelDimension]] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """Preprocesses a single image.""" # All transformations expect numpy arrays. image = to_numpy_array(image) if is_scaled_image(image) and do_rescale: logger.warning_once( "It looks like you are trying to rescale already rescaled images. If the input" " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." ) if input_data_format is None: input_data_format = infer_channel_dimension_format(image) image = self._preprocess( image=image, do_reduce_labels=False, do_resize=do_resize, size=size, resample=resample, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, input_data_format=input_data_format, ) if data_format is not None: image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) return image def _preprocess_mask( self, segmentation_map: ImageInput, do_reduce_labels: bool = None, do_resize: bool = None, size: Dict[str, int] = None, input_data_format: Optional[Union[str, ChannelDimension]] = None, ) -> np.ndarray: """Preprocesses a single mask.""" segmentation_map = to_numpy_array(segmentation_map) # Add channel dimension if missing - needed for certain transformations if segmentation_map.ndim == 2: added_channel_dim = True segmentation_map = segmentation_map[None, ...] input_data_format = ChannelDimension.FIRST else: added_channel_dim = False if input_data_format is None: input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1) # reduce zero label if needed segmentation_map = self._preprocess( image=segmentation_map, do_reduce_labels=do_reduce_labels, do_resize=do_resize, resample=PILImageResampling.NEAREST, size=size, do_rescale=False, do_normalize=False, input_data_format=input_data_format, ) # Remove extra channel dimension if added for processing if added_channel_dim: segmentation_map = segmentation_map.squeeze(0) segmentation_map = segmentation_map.astype(np.int64) return segmentation_map def __call__(self, images, segmentation_maps=None, **kwargs): """ Preprocesses a batch of images and optionally segmentation maps. Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be passed in as positional arguments. """ return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs) def preprocess( self, images: ImageInput, segmentation_maps: Optional[ImageInput] = None, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, List[float]]] = None, image_std: Optional[Union[float, List[float]]] = None, do_reduce_labels: Optional[bool] = None, return_tensors: Optional[Union[str, TensorType]] = None, data_format: ChannelDimension = ChannelDimension.FIRST, input_data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs, ) -> PIL.Image.Image: """ Preprocess an image or batch of images. 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`. segmentation_maps (`ImageInput`, *optional*): Segmentation map to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after `resize` is applied. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image values between [0 - 1]. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Image mean. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Image standard deviation. do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. 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`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. """ do_resize = do_resize if do_resize is not None else self.do_resize do_rescale = do_rescale if do_rescale is not None else self.do_rescale do_normalize = do_normalize if do_normalize is not None else self.do_normalize do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels resample = resample if resample is not None else self.resample size = size if size is not None else self.size rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std images = make_list_of_images(images) validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) if segmentation_maps is not None: segmentation_maps = make_list_of_images(segmentation_maps, expected_ndims=2) 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." ) validate_preprocess_arguments( do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, do_resize=do_resize, size=size, resample=resample, ) images = [ self._preprocess_image( image=img, do_resize=do_resize, resample=resample, size=size, do_rescale=do_rescale, rescale_factor=rescale_factor, do_normalize=do_normalize, image_mean=image_mean, image_std=image_std, data_format=data_format, input_data_format=input_data_format, ) for img in images ] data = {"pixel_values": images} if segmentation_maps is not None: segmentation_maps = [ self._preprocess_mask( segmentation_map=segmentation_map, do_reduce_labels=do_reduce_labels, do_resize=do_resize, size=size, input_data_format=input_data_format, ) for segmentation_map in segmentation_maps ] data["labels"] = segmentation_maps return BatchFeature(data=data, tensor_type=return_tensors) # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->Segformer def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None): """ Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch. Args: outputs ([`SegformerForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`List[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. """ # TODO: add support for other frameworks logits = outputs.logits # Resize logits and compute semantic segmentation maps if target_sizes is not None: if len(logits) != len(target_sizes): raise ValueError( "Make sure that you pass in as many target sizes as the batch dimension of the logits" ) if is_torch_tensor(target_sizes): target_sizes = target_sizes.numpy() semantic_segmentation = [] for idx in range(len(logits)): resized_logits = torch.nn.functional.interpolate( logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False ) semantic_map = resized_logits[0].argmax(dim=0) semantic_segmentation.append(semantic_map) else: semantic_segmentation = logits.argmax(dim=1) semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] return semantic_segmentation

transformers/src/transformers/models/segformer/image_processing_segformer.py/0

{ "file_path": "transformers/src/transformers/models/segformer/image_processing_segformer.py", "repo_id": "transformers", "token_count": 10219 }

331

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Feature extractor class for Speech2Text """ from typing import List, Optional, Union import numpy as np from ...audio_utils import mel_filter_bank, spectrogram, window_function from ...feature_extraction_sequence_utils import SequenceFeatureExtractor from ...feature_extraction_utils import BatchFeature from ...utils import PaddingStrategy, TensorType, is_speech_available, logging if is_speech_available(): import torch import torchaudio.compliance.kaldi as ta_kaldi logger = logging.get_logger(__name__) class Speech2TextFeatureExtractor(SequenceFeatureExtractor): r""" Constructs a Speech2Text feature extractor. This feature extractor inherits from [`Speech2TextFeatureExtractor`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. This class extracts mel-filter bank features from raw speech using TorchAudio if installed or using numpy otherwise, and applies utterance-level cepstral mean and variance normalization to the extracted features. Args: feature_size (`int`, *optional*, defaults to 80): The feature dimension of the extracted features. sampling_rate (`int`, *optional*, defaults to 16000): The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). num_mel_bins (`int`, *optional*, defaults to 80): Number of Mel-frequency bins. padding_value (`float`, *optional*, defaults to 0.0): The value that is used to fill the padding vectors. do_ceptral_normalize (`bool`, *optional*, defaults to `True`): Whether or not to apply utterance-level cepstral mean and variance normalization to extracted features. normalize_means (`bool`, *optional*, defaults to `True`): Whether or not to zero-mean normalize the extracted features. normalize_vars (`bool`, *optional*, defaults to `True`): Whether or not to unit-variance normalize the extracted features. """ model_input_names = ["input_features", "attention_mask"] def __init__( self, feature_size=80, sampling_rate=16000, num_mel_bins=80, padding_value=0.0, do_ceptral_normalize=True, normalize_means=True, normalize_vars=True, **kwargs, ): super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) self.num_mel_bins = num_mel_bins self.do_ceptral_normalize = do_ceptral_normalize self.normalize_means = normalize_means self.normalize_vars = normalize_vars self.return_attention_mask = True if not is_speech_available(): mel_filters = mel_filter_bank( num_frequency_bins=256, num_mel_filters=self.num_mel_bins, min_frequency=20, max_frequency=sampling_rate // 2, sampling_rate=sampling_rate, norm=None, mel_scale="kaldi", triangularize_in_mel_space=True, ) self.mel_filters = np.pad(mel_filters, ((0, 1), (0, 0))) self.window = window_function(400, "povey", periodic=False) def _extract_fbank_features( self, waveform: np.ndarray, ) -> np.ndarray: """ Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs and hence the waveform should not be normalized before feature extraction. """ waveform = waveform * (2**15) # Kaldi compliance: 16-bit signed integers if is_speech_available(): waveform = torch.from_numpy(waveform).unsqueeze(0) features = ta_kaldi.fbank(waveform, num_mel_bins=self.num_mel_bins, sample_frequency=self.sampling_rate) features = features.numpy() else: waveform = np.squeeze(waveform) features = spectrogram( waveform, self.window, frame_length=400, hop_length=160, fft_length=512, power=2.0, center=False, preemphasis=0.97, mel_filters=self.mel_filters, log_mel="log", mel_floor=1.192092955078125e-07, remove_dc_offset=True, ).T return features @staticmethod def utterance_cmvn( x: np.ndarray, input_length: int, normalize_means: Optional[bool] = True, normalize_vars: Optional[bool] = True, padding_value: float = 0.0, ) -> np.ndarray: # make sure we normalize float32 arrays if normalize_means: mean = x[:input_length].mean(axis=0) x = np.subtract(x, mean) if normalize_vars: std = x[:input_length].std(axis=0) x = np.divide(x, std) if input_length < x.shape[0]: x[input_length:] = padding_value # make sure array is in float32 x = x.astype(np.float32) return x def normalize( self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None ) -> List[np.ndarray]: lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features] return [ self.utterance_cmvn(x, n, self.normalize_means, self.normalize_vars, self.padding_value) for x, n in zip(input_features, lengths) ] def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, truncation: bool = False, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, sampling_rate: Optional[int] = None, return_attention_mask: Optional[bool] = None, **kwargs, ) -> BatchFeature: """ Main method to featurize and prepare for the model one or several sequence(s). Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not stereo, i.e. single float per timestep. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): 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`): Activates truncation to cut input sequences longer than *max_length* to *max_length*. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. return_attention_mask (`bool`, *optional*): Whether to return the attention mask. If left to the default, will return the attention mask according to the specific feature_extractor's default. [What are attention masks?](../glossary#attention-mask) <Tip> For Speech2TextTransformer models, `attention_mask` should always be passed for batched inference, to avoid subtle bugs. </Tip> return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. sampling_rate (`int`, *optional*): The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass `sampling_rate` at the forward call to prevent silent errors. padding_value (`float`, defaults to 0.0): The value that is used to fill the padding values / vectors. """ if sampling_rate is not None: if sampling_rate != self.sampling_rate: raise ValueError( f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with" f" {self.sampling_rate} and not {sampling_rate}." ) else: logger.warning( "It is strongly recommended to pass the `sampling_rate` argument to this function. " "Failing to do so can result in silent errors that might be hard to debug." ) is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 if is_batched_numpy and len(raw_speech.shape) > 2: raise ValueError(f"Only mono-channel audio is supported for input to {self}") is_batched = is_batched_numpy or ( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list))) ) if is_batched: raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech, dtype=np.float32) elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64): raw_speech = raw_speech.astype(np.float32) # always return batch if not is_batched: raw_speech = [raw_speech] # extract fbank features features = [self._extract_fbank_features(waveform) for waveform in raw_speech] # convert into correct format for padding encoded_inputs = BatchFeature({"input_features": features}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, truncation=truncation, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, **kwargs, ) # make sure list is in array format input_features = padded_inputs.get("input_features") if isinstance(input_features[0], list): padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features] attention_mask = padded_inputs.get("attention_mask") if attention_mask is not None: padded_inputs["attention_mask"] = [np.asarray(array, dtype=np.int32) for array in attention_mask] # Utterance-level cepstral mean and variance normalization if self.do_ceptral_normalize: attention_mask = ( np.array(attention_mask, dtype=np.int32) if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD else None ) padded_inputs["input_features"] = self.normalize( padded_inputs["input_features"], attention_mask=attention_mask ) if return_tensors is not None: padded_inputs = padded_inputs.convert_to_tensors(return_tensors) return padded_inputs

transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py/0

{ "file_path": "transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py", "repo_id": "transformers", "token_count": 5608 }

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# coding=utf-8 # Copyright 2023 The Fairseq Authors, Microsoft Research, 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. """Number Normalizer class for SpeechT5.""" import re class EnglishNumberNormalizer: def __init__(self): self.ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"] self.teens = [ "", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen", "seventeen", "eighteen", "nineteen", ] self.tens = ["", "ten", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"] self.thousands = [ "", "thousand", "million", "billion", "trillion", "quadrillion", "quintillion", "sextillion", "septillion", "octillion", "nonillion", "decillion", ] # Define a dictionary to map currency symbols to their names # Top most traded currencies according to # https://en.wikipedia.org/wiki/Template:Most_traded_currencies self.currency_symbols = { "$": " dollars", "€": " euros", "£": " pounds", "¢": " cents", "¥": " japanese yen", "﷼": " saudi riyal", "₹": " indian rupees", "₽": " russian rubles", "฿": " thai baht", "₺": " turkish liras", "₴": " ukrainian hryvnia", "₣": " swiss francs", "₡": " costa rican colon", "₱": " philippine peso", "₪": " israeli shekels", "₮": " mongolian tögrög", "₩": " south korean won", "₦": " nigerian naira", "₫": " vietnamese Đồng", } def spell_number(self, num): if num == 0: return "zero" parts = [] for i in range(0, len(self.thousands)): if num % 1000 != 0: part = "" hundreds = num % 1000 // 100 tens_units = num % 100 if hundreds > 0: part += self.ones[hundreds] + " hundred" if tens_units > 0: part += " and " if tens_units > 10 and tens_units < 20: part += self.teens[tens_units - 10] else: tens_digit = self.tens[tens_units // 10] ones_digit = self.ones[tens_units % 10] if tens_digit: part += tens_digit if ones_digit: if tens_digit: part += " " part += ones_digit parts.append(part) num //= 1000 return " ".join(reversed(parts)) def convert(self, number): """ Converts an individual number passed in string form to spelt-out form """ if "." in number: integer_part, decimal_part = number.split(".") else: integer_part, decimal_part = number, "00" # Extract currency symbol if present currency_symbol = "" for symbol, name in self.currency_symbols.items(): if integer_part.startswith(symbol): currency_symbol = name integer_part = integer_part[len(symbol) :] break if integer_part.startswith("-"): if integer_part[1:].startswith(symbol): currency_symbol = name integer_part = "-" + integer_part[len(symbol) + 1 :] break # Extract 'minus' prefix for negative numbers minus_prefix = "" if integer_part.startswith("-"): minus_prefix = "minus " integer_part = integer_part[1:] elif integer_part.startswith("minus"): minus_prefix = "minus " integer_part = integer_part[len("minus") :] percent_suffix = "" if "%" in integer_part or "%" in decimal_part: percent_suffix = " percent" integer_part = integer_part.replace("%", "") decimal_part = decimal_part.replace("%", "") integer_part = integer_part.zfill(3 * ((len(integer_part) - 1) // 3 + 1)) parts = [] for i in range(0, len(integer_part), 3): chunk = int(integer_part[i : i + 3]) if chunk > 0: part = self.spell_number(chunk) unit = self.thousands[len(integer_part[i:]) // 3 - 1] if unit: part += " " + unit parts.append(part) spelled_integer = " ".join(parts) # Format the spelt-out number based on conditions, such as: # If it has decimal parts, currency symbol, minus prefix, etc if decimal_part == "00": return ( f"{minus_prefix}{spelled_integer}{percent_suffix}{currency_symbol}" if minus_prefix or currency_symbol else f"{spelled_integer}{percent_suffix}" ) else: spelled_decimal = " ".join([self.spell_number(int(digit)) for digit in decimal_part]) return ( f"{minus_prefix}{spelled_integer} point {spelled_decimal}{percent_suffix}{currency_symbol}" if minus_prefix or currency_symbol else f"{minus_prefix}{spelled_integer} point {spelled_decimal}{percent_suffix}" ) def __call__(self, text): """ Convert numbers / number-like quantities in a string to their spelt-out counterparts """ # Form part of the pattern for all currency symbols pattern = r"(?<!\w)(-?\$?\€?\£?\¢?\¥?\₹?\₽?\฿?\₺?\₴?\₣?\₡?\₱?\₪?\₮?\₩?\₦?\₫?\﷼?\d+(?:\.\d{1,2})?%?)(?!\w)" # Find and replace commas in numbers (15,000 -> 15000, etc) text = re.sub(r"(\d+,\d+)", lambda match: match.group(1).replace(",", ""), text) # Use regex to find and replace numbers in the text converted_text = re.sub(pattern, lambda match: self.convert(match.group(1)), text) converted_text = re.sub(" +", " ", converted_text) return converted_text

transformers/src/transformers/models/speecht5/number_normalizer.py/0

{ "file_path": "transformers/src/transformers/models/speecht5/number_normalizer.py", "repo_id": "transformers", "token_count": 3534 }

333

# Copyright 2020 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. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _import_structure = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_t5"] = ["T5Tokenizer"] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["tokenization_t5_fast"] = ["T5TokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_t5"] = [ "T5_PRETRAINED_MODEL_ARCHIVE_LIST", "T5EncoderModel", "T5ForConditionalGeneration", "T5Model", "T5PreTrainedModel", "load_tf_weights_in_t5", "T5ForQuestionAnswering", "T5ForSequenceClassification", "T5ForTokenClassification", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_t5"] = [ "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", "TFT5EncoderModel", "TFT5ForConditionalGeneration", "TFT5Model", "TFT5PreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_t5"] = [ "FlaxT5EncoderModel", "FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel", ] if TYPE_CHECKING: from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config, T5OnnxConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_t5 import T5Tokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_t5_fast import T5TokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_t5 import ( T5_PRETRAINED_MODEL_ARCHIVE_LIST, T5EncoderModel, T5ForConditionalGeneration, T5ForQuestionAnswering, T5ForSequenceClassification, T5ForTokenClassification, T5Model, T5PreTrainedModel, load_tf_weights_in_t5, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_t5 import ( TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model, TFT5PreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_t5 import ( FlaxT5EncoderModel, FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

transformers/src/transformers/models/t5/__init__.py/0

{ "file_path": "transformers/src/transformers/models/t5/__init__.py", "repo_id": "transformers", "token_count": 1938 }

334

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert VideoMAE checkpoints from the original repository: https://github.com/MCG-NJU/VideoMAE""" import argparse import json import gdown import numpy as np import torch from huggingface_hub import hf_hub_download from transformers import ( VideoMAEConfig, VideoMAEForPreTraining, VideoMAEForVideoClassification, VideoMAEImageProcessor, ) def get_videomae_config(model_name): config = VideoMAEConfig() set_architecture_configs(model_name, config) if "finetuned" not in model_name: config.use_mean_pooling = False if "finetuned" in model_name: repo_id = "huggingface/label-files" if "kinetics" in model_name: config.num_labels = 400 filename = "kinetics400-id2label.json" elif "ssv2" in model_name: config.num_labels = 174 filename = "something-something-v2-id2label.json" else: raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.") id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} return config def set_architecture_configs(model_name, config): if "small" in model_name: config.hidden_size = 384 config.intermediate_size = 1536 config.num_hidden_layers = 12 config.num_attention_heads = 16 config.decoder_num_hidden_layers = 12 config.decoder_num_attention_heads = 3 config.decoder_hidden_size = 192 config.decoder_intermediate_size = 768 elif "large" in model_name: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 config.decoder_num_hidden_layers = 12 config.decoder_num_attention_heads = 8 config.decoder_hidden_size = 512 config.decoder_intermediate_size = 2048 elif "huge" in model_name: config.hidden_size = 1280 config.intermediate_size = 5120 config.num_hidden_layers = 32 config.num_attention_heads = 16 config.decoder_num_hidden_layers = 12 config.decoder_num_attention_heads = 8 config.decoder_hidden_size = 640 config.decoder_intermediate_size = 2560 elif "base" not in model_name: raise ValueError('Model name should include either "small", "base", "large", or "huge"') def rename_key(name): if "encoder." in name: name = name.replace("encoder.", "") if "cls_token" in name: name = name.replace("cls_token", "videomae.embeddings.cls_token") if "decoder_pos_embed" in name: name = name.replace("decoder_pos_embed", "decoder.decoder_pos_embed") if "pos_embed" in name and "decoder" not in name: name = name.replace("pos_embed", "videomae.embeddings.position_embeddings") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "videomae.embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "videomae.embeddings.norm") if "decoder.blocks" in name: name = name.replace("decoder.blocks", "decoder.decoder_layers") if "blocks" in name: name = name.replace("blocks", "videomae.encoder.layer") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name and "bias" not in name: name = name.replace("attn", "attention.self") if "attn" in name: name = name.replace("attn", "attention.attention") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "decoder_embed" in name: name = name.replace("decoder_embed", "decoder.decoder_embed") if "decoder_norm" in name: name = name.replace("decoder_norm", "decoder.decoder_norm") if "decoder_pred" in name: name = name.replace("decoder_pred", "decoder.decoder_pred") if "norm.weight" in name and "decoder" not in name and "fc" not in name: name = name.replace("norm.weight", "videomae.layernorm.weight") if "norm.bias" in name and "decoder" not in name and "fc" not in name: name = name.replace("norm.bias", "videomae.layernorm.bias") if "head" in name and "decoder" not in name: name = name.replace("head", "classifier") return name def convert_state_dict(orig_state_dict, config): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if key.startswith("encoder."): key = key.replace("encoder.", "") if "qkv" in key: key_split = key.split(".") if key.startswith("decoder.blocks"): dim = config.decoder_hidden_size layer_num = int(key_split[2]) prefix = "decoder.decoder_layers." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] else: dim = config.hidden_size layer_num = int(key_split[1]) prefix = "videomae.encoder.layer." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] else: orig_state_dict[rename_key(key)] = val return orig_state_dict # We will verify our results on a video of eating spaghetti # Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227] def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset" ) video = np.load(file) return list(video) def convert_videomae_checkpoint(checkpoint_url, pytorch_dump_folder_path, model_name, push_to_hub): config = get_videomae_config(model_name) if "finetuned" in model_name: model = VideoMAEForVideoClassification(config) else: model = VideoMAEForPreTraining(config) # download original checkpoint, hosted on Google Drive output = "pytorch_model.bin" gdown.cached_download(checkpoint_url, output, quiet=False) files = torch.load(output, map_location="cpu") if "model" in files: state_dict = files["model"] else: state_dict = files["module"] new_state_dict = convert_state_dict(state_dict, config) model.load_state_dict(new_state_dict) model.eval() # verify model on basic input image_processor = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) video = prepare_video() inputs = image_processor(video, return_tensors="pt") if "finetuned" not in model_name: local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt") inputs["bool_masked_pos"] = torch.load(local_path) outputs = model(**inputs) logits = outputs.logits model_names = [ "videomae-small-finetuned-kinetics", "videomae-small-finetuned-ssv2", # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600) "videomae-base-short", "videomae-base-short-finetuned-kinetics", "videomae-base", "videomae-base-finetuned-kinetics", "videomae-large", "videomae-large-finetuned-kinetics", "videomae-huge-finetuned-kinetics", # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400) "videomae-base-short-ssv2", "videomae-base-short-finetuned-ssv2", "videomae-base-ssv2", "videomae-base-finetuned-ssv2", ] # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5] if model_name == "videomae-small-finetuned-kinetics": expected_shape = torch.Size([1, 400]) expected_slice = torch.tensor([-0.9291, -0.4061, -0.9307]) elif model_name == "videomae-small-finetuned-ssv2": expected_shape = torch.Size([1, 174]) expected_slice = torch.tensor([0.2671, -0.4689, -0.8235]) elif model_name == "videomae-base": expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]) elif model_name == "videomae-base-short": expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]) # we verified the loss both for normalized and unnormalized targets for this one expected_loss = torch.tensor([0.5142]) if config.norm_pix_loss else torch.tensor([0.6469]) elif model_name == "videomae-large": expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]) elif model_name == "videomae-large-finetuned-kinetics": expected_shape = torch.Size([1, 400]) expected_slice = torch.tensor([0.0771, 0.0011, -0.3625]) elif model_name == "videomae-huge-finetuned-kinetics": expected_shape = torch.Size([1, 400]) expected_slice = torch.tensor([0.2433, 0.1632, -0.4894]) elif model_name == "videomae-base-short-finetuned-kinetics": expected_shape = torch.Size([1, 400]) expected_slice = torch.tensor([0.6588, 0.0990, -0.2493]) elif model_name == "videomae-base-finetuned-kinetics": expected_shape = torch.Size([1, 400]) expected_slice = torch.tensor([0.3669, -0.0688, -0.2421]) elif model_name == "videomae-base-short-ssv2": expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]) elif model_name == "videomae-base-short-finetuned-ssv2": expected_shape = torch.Size([1, 174]) expected_slice = torch.tensor([-0.0537, -0.1539, -0.3266]) elif model_name == "videomae-base-ssv2": expected_shape = torch.Size([1, 1408, 1536]) expected_slice = torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]) elif model_name == "videomae-base-finetuned-ssv2": expected_shape = torch.Size([1, 174]) expected_slice = torch.tensor([0.1961, -0.8337, -0.6389]) else: raise ValueError(f"Model name not supported. Should be one of {model_names}") # verify logits assert logits.shape == expected_shape if "finetuned" in model_name: assert torch.allclose(logits[0, :3], expected_slice, atol=1e-4) else: print("Logits:", logits[0, :3, :3]) assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4) print("Logits ok!") # verify loss, if applicable if model_name == "videomae-base-short": loss = outputs.loss assert torch.allclose(loss, expected_loss, atol=1e-4) print("Loss ok!") if pytorch_dump_folder_path is not None: print(f"Saving model and image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) model.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print("Pushing to the hub...") model.push_to_hub(model_name, organization="nielsr") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4", type=str, help=( "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct" " download link." ), ) parser.add_argument( "--pytorch_dump_folder_path", default="/Users/nielsrogge/Documents/VideoMAE/Test", type=str, help="Path to the output PyTorch model directory.", ) parser.add_argument("--model_name", default="videomae-base", type=str, help="Name of the model.") parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

transformers/src/transformers/models/videomae/convert_videomae_to_pytorch.py/0

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# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto.configuration_auto import AutoConfig if TYPE_CHECKING: from ... import PreTrainedTokenizerBase, TensorType logger = logging.get_logger(__name__) class VisionEncoderDecoderConfig(PretrainedConfig): r""" [`VisionEncoderDecoderConfig`] is the configuration class to store the configuration of a [`VisionEncoderDecoderModel`]. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the specified arguments, defining the encoder and decoder configs. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: kwargs (*optional*): Dictionary of keyword arguments. Notably: - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the encoder config. - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the decoder config. Examples: ```python >>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel >>> # Initializing a ViT & BERT style configuration >>> config_encoder = ViTConfig() >>> config_decoder = BertConfig() >>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) >>> # Initializing a ViTBert model (with random weights) from a ViT & google-bert/bert-base-uncased style configurations >>> model = VisionEncoderDecoderModel(config=config) >>> # Accessing the model configuration >>> config_encoder = model.config.encoder >>> config_decoder = model.config.decoder >>> # set decoder config to causal lm >>> config_decoder.is_decoder = True >>> config_decoder.add_cross_attention = True >>> # Saving the model, including its configuration >>> model.save_pretrained("my-model") >>> # loading model and config from pretrained folder >>> encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model") >>> model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" model_type = "vision-encoder-decoder" is_composition = True def __init__(self, **kwargs): super().__init__(**kwargs) if "encoder" not in kwargs or "decoder" not in kwargs: raise ValueError( f"A configuraton of type {self.model_type} cannot be instantiated because " f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}" ) encoder_config = kwargs.pop("encoder") encoder_model_type = encoder_config.pop("model_type") decoder_config = kwargs.pop("decoder") decoder_model_type = decoder_config.pop("model_type") self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config) self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config) self.is_encoder_decoder = True @classmethod def from_encoder_decoder_configs( cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs ) -> PretrainedConfig: r""" Instantiate a [`VisionEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and decoder model configuration. Returns: [`VisionEncoderDecoderConfig`]: An instance of a configuration object """ logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") decoder_config.is_decoder = True decoder_config.add_cross_attention = True return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) class VisionEncoderDecoderEncoderOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4 @property def outputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}}) class VisionEncoderDecoderDecoderOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict() common_inputs["input_ids"] = {0: "batch", 1: "past_decoder_sequence + sequence"} common_inputs["attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} common_inputs["encoder_hidden_states"] = {0: "batch", 1: "encoder_sequence"} return common_inputs def generate_dummy_inputs( self, tokenizer: "PreTrainedTokenizerBase", batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional["TensorType"] = None, ) -> Mapping[str, Any]: import torch common_inputs = OrderedDict() dummy_input = super().generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework ) batch, encoder_sequence = dummy_input["input_ids"].shape encoder_hidden_states_shape = (batch, encoder_sequence, self._config.encoder_hidden_size) common_inputs["input_ids"] = dummy_input.pop("input_ids") common_inputs["attention_mask"] = dummy_input.pop("attention_mask") common_inputs["encoder_hidden_states"] = torch.zeros(encoder_hidden_states_shape) return common_inputs class VisionEncoderDecoderOnnxConfig(OnnxConfig): @property def inputs(self) -> None: pass def get_encoder_config(self, encoder_config: PretrainedConfig) -> OnnxConfig: r""" Returns ONNX encoder config for `VisionEncoderDecoder` model. Args: encoder_config (`PretrainedConfig`): The encoder model's configuration to use when exporting to ONNX. Returns: [`VisionEncoderDecoderEncoderOnnxConfig`]: An instance of the ONNX configuration object """ return VisionEncoderDecoderEncoderOnnxConfig(encoder_config) def get_decoder_config( self, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, feature: str = "default" ) -> OnnxConfig: r""" Returns ONNX decoder config for `VisionEncoderDecoder` model. Args: encoder_config (`PretrainedConfig`): The encoder model's configuration to use when exporting to ONNX. decoder_config (`PretrainedConfig`): The decoder model's configuration to use when exporting to ONNX feature (`str`, *optional*): The type of feature to export the model with. Returns: [`VisionEncoderDecoderDecoderOnnxConfig`]: An instance of the ONNX configuration object. """ decoder_config.encoder_hidden_size = encoder_config.hidden_size return VisionEncoderDecoderDecoderOnnxConfig(decoder_config, feature)

transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py/0

{ "file_path": "transformers/src/transformers/models/vision_encoder_decoder/configuration_vision_encoder_decoder.py", "repo_id": "transformers", "token_count": 3125 }

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# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ViT checkpoints trained with the DINO method.""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) # here we list all keys to be renamed (original name on the left, our name on the right) def create_rename_keys(config, base_model=False): rename_keys = [] for i in range(config.num_hidden_layers): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")) rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")) rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")) rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")) rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")) rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")) rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")) rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")) rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")) # projection layer + position embeddings rename_keys.extend( [ ("cls_token", "vit.embeddings.cls_token"), ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"), ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"), ("pos_embed", "vit.embeddings.position_embeddings"), ] ) if base_model: # layernorm + pooler rename_keys.extend( [ ("norm.weight", "layernorm.weight"), ("norm.bias", "layernorm.bias"), ] ) # if just the base model, we should remove "vit" from all keys that start with "vit" rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys] else: # layernorm + classification head rename_keys.extend( [ ("norm.weight", "vit.layernorm.weight"), ("norm.bias", "vit.layernorm.bias"), ("head.weight", "classifier.weight"), ("head.bias", "classifier.bias"), ] ) return rename_keys # we split up the matrix of each encoder layer into queries, keys and values def read_in_q_k_v(state_dict, config, base_model=False): for i in range(config.num_hidden_layers): if base_model: prefix = "" else: prefix = "vit." # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight") in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias") # next, add query, keys and values (in that order) to the state dict state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[ : config.hidden_size, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[ -config.hidden_size :, : ] state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :] def remove_classification_head_(state_dict): ignore_keys = ["head.weight", "head.bias"] for k in ignore_keys: state_dict.pop(k, None) def rename_key(dct, old, new): val = dct.pop(old) dct[new] = val # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @torch.no_grad() def convert_vit_checkpoint(model_name, pytorch_dump_folder_path, base_model=True): """ Copy/paste/tweak model's weights to our ViT structure. """ # define default ViT configuration config = ViTConfig() # patch_size if model_name[-1] == "8": config.patch_size = 8 # set labels if required if not base_model: config.num_labels = 1000 repo_id = "huggingface/label-files" filename = "imagenet-1k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} # size of the architecture if model_name in ["dino_vits8", "dino_vits16"]: config.hidden_size = 384 config.intermediate_size = 1536 config.num_hidden_layers = 12 config.num_attention_heads = 6 # load original model from torch hub original_model = torch.hub.load("facebookresearch/dino:main", model_name) original_model.eval() # load state_dict of original model, remove and rename some keys state_dict = original_model.state_dict() if base_model: remove_classification_head_(state_dict) rename_keys = create_rename_keys(config, base_model=base_model) for src, dest in rename_keys: rename_key(state_dict, src, dest) read_in_q_k_v(state_dict, config, base_model) # load HuggingFace model if base_model: model = ViTModel(config, add_pooling_layer=False).eval() else: model = ViTForImageClassification(config).eval() model.load_state_dict(state_dict) # Check outputs on an image, prepared by ViTImageProcessor image_processor = ViTImageProcessor() encoding = image_processor(images=prepare_img(), return_tensors="pt") pixel_values = encoding["pixel_values"] outputs = model(pixel_values) if base_model: final_hidden_state_cls_token = original_model(pixel_values) assert torch.allclose(final_hidden_state_cls_token, outputs.last_hidden_state[:, 0, :], atol=1e-1) else: logits = original_model(pixel_values) assert logits.shape == outputs.logits.shape assert torch.allclose(logits, outputs.logits, atol=1e-3) Path(pytorch_dump_folder_path).mkdir(exist_ok=True) print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving image processor to {pytorch_dump_folder_path}") image_processor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="dino_vitb16", type=str, help="Name of the model trained with DINO you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--base_model", action="store_true", help="Whether to only convert the base model (no projection head weights).", ) parser.set_defaults(base_model=True) args = parser.parse_args() convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)

transformers/src/transformers/models/vit/convert_dino_to_pytorch.py/0

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