From c2b9af32775fd28f693dd1b572142935efd31b99 Mon Sep 17 00:00:00 2001 From: Martin Krasser Date: Wed, 7 Sep 2022 15:10:28 +0200 Subject: [PATCH] Perceiver AR - closes #20 --- README.md | 197 +++- docs/building-blocks.md | 11 +- docs/dataset-preproc.md | 15 +- docs/docker-image.md | 2 +- docs/images/perceiver-ar.png | Bin 0 -> 84113 bytes docs/images/small-perceiver-ar.png | Bin 0 -> 58905 bytes docs/images/small-perceiver-io.png | Bin 0 -> 58920 bytes docs/images/small-perceiver.png | Bin 0 -> 46726 bytes docs/model-construction.md | 156 ++- docs/pretrained-models.md | 28 +- docs/training-examples.md | 117 +- notebooks/inference_examples.ipynb | 1027 ++++++++++------- perceiver/data/text/__init__.py | 1 + perceiver/data/text/collator.py | 6 +- perceiver/data/text/common.py | 31 +- perceiver/data/text/enwik8.py | 52 + perceiver/data/text/imdb.py | 14 +- perceiver/data/text/wikipedia.py | 4 +- perceiver/data/text/wikitext.py | 39 +- perceiver/model/core/classifier.py | 26 + perceiver/model/core/config.py | 18 +- perceiver/model/core/modules.py | 329 ++++-- perceiver/model/core/position.py | 45 + perceiver/model/core/utils.py | 29 +- perceiver/model/image/classifier.py | 3 +- perceiver/model/text/classifier.py | 15 +- perceiver/model/text/clm.py | 214 ++++ perceiver/model/text/common.py | 13 +- perceiver/model/text/language.py | 220 +--- perceiver/model/text/mlm.py | 258 +++++ perceiver/model/text/utils.py | 60 - perceiver/scripts/text/__init__.py | 1 + perceiver/scripts/text/clm.py | 24 + perceiver/scripts/text/{lm.py => mlm.py} | 4 +- perceiver/scripts/text/preproc.py | 12 + pyproject.toml | 2 +- tests/language_model_conversion_test.py | 10 +- ...n_test.py => masked_sample_filler_test.py} | 22 +- 38 files changed, 2070 insertions(+), 935 deletions(-) create mode 100644 docs/images/perceiver-ar.png create mode 100644 docs/images/small-perceiver-ar.png create mode 100644 docs/images/small-perceiver-io.png create mode 100644 docs/images/small-perceiver.png create mode 100644 perceiver/data/text/enwik8.py create mode 100644 perceiver/model/core/classifier.py create mode 100644 perceiver/model/core/position.py create mode 100644 perceiver/model/text/clm.py create mode 100644 perceiver/model/text/mlm.py delete mode 100644 perceiver/model/text/utils.py create mode 100644 perceiver/scripts/text/clm.py rename perceiver/scripts/text/{lm.py => mlm.py} (94%) rename tests/{masked_sample_prediction_test.py => masked_sample_filler_test.py} (67%) diff --git a/README.md b/README.md index 102bfef..cb72cfc 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,42 @@ -# Perceiver IO +# Perceiver, Perceiver IO and Perceiver AR -This library is a PyTorch and PyTorch Lightning implementation of +This repository is a PyTorch and PyTorch Lightning implementation of -- [Perceiver: General Perception with Iterative Attention](https://arxiv.org/abs/2103.03206) and -- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) + + + + + + + + + + + + + +
+ Perceiver: General Perception with Iterative Attention + (paper, + video) + Perceiver
+ Perceiver IO: A General Architecture for Structured Inputs & Outputs + (paper, + blog post) + Perceiver IO
+ General-purpose, long-context autoregressive modeling with Perceiver AR + (paper, + blog post) + Perceiver AR
-The codebase is designed for easy extension to new tasks and datasets. The integration with [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) -supports model training at scale. The command line interface is implemented with the [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/cli/lightning_cli.html). -Pretrained parameters can be imported for [some models](docs/pretrained-models.md) from the 🤗 Hub. Datasets used for -model training are 🤗 [Datasets](https://huggingface.co/docs/datasets) wrapped into PyTorch Lightning data modules. -For NLP tasks, this library also supports 🤗 [fast tokenizers](https://huggingface.co/docs/transformers/fast_tokenizers) -and the 🤗 Perceiver UTF-8 bytes tokenizer. +The codebase is modular and designed for easy extension to new tasks and datasets. The integration with +[PyTorch Lightning](https://pytorch-lightning.readthedocs.io/en/stable/) supports model training at scale. The command +line interface is implemented with the [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/cli/lightning_cli.html). + +[Pretrained models](docs/pretrained-models.md) can be imported from the 🤗 Hub. Datasets used for model training +are 🤗 [Datasets](https://huggingface.co/docs/datasets) wrapped into PyTorch Lightning data modules. For NLP tasks, +this library also supports 🤗 [fast tokenizers](https://huggingface.co/docs/transformers/fast_tokenizers) and the +🤗 Perceiver UTF-8 bytes tokenizer. ## Installation @@ -23,7 +49,7 @@ pip install perceiver-io[image,text] ### From sources Installation from sources requires a [Miniconda](https://docs.conda.io/en/latest/miniconda.html) and a -[Poetry](https://python-poetry.org/docs/master/#installation) (1.2.0b2 or higher) installation. +[Poetry](https://python-poetry.org/docs/#installation) (1.2.0 or higher) installation. ```shell conda env create -f environment.yml @@ -33,12 +59,157 @@ poetry install --all-extras ### Docker image +```shell +docker pull ghcr.io/krasserm/perceiver-io:latest +``` + See [Docker image](docs/docker-image.md) for details. ## Documentation -- [Model construction](docs/model-construction.md) - [Pretrained models](docs/pretrained-models.md) +- [Model construction](docs/model-construction.md) +- [Building blocks](docs/building-blocks.md) - [Training examples](docs/training-examples.md) - [Inference examples](notebooks/inference_examples.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/notebooks/inference_examples.ipynb) -- [Building blocks](docs/building-blocks.md) + +## Getting started + +Here's a minimal example for autoregressive language modeling with Perceiver AR. A small language model (30.7M parameters) +is trained on the WikiText-103-raw dataset and then used to generate text from a prompt. Input text is tokenized into +raw UTF-8 bytes, the model also predicts the raw UTF-8 bytes of generated text. More details about Perceiver AR and +Perceiver IO model construction, training and inference are covered in the [documentation](#documentation). + +### Training + +The command line interface is implemented with [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/stable/cli/lightning_cli.html). +Model training can be started with: + +```shell +python -m perceiver.scripts.text.clm fit \ + --model.num_latents=512 \ + --model.num_channels=512 \ + --model.num_self_attention_layers=8 \ + --model.cross_attention_dropout=0.5 \ + --data=WikiTextDataModule \ + --data.tokenizer=deepmind/language-perceiver \ + --data.max_seq_len=4096 \ + --data.batch_size=16 \ + --data.task=clm \ + --optimizer=Adam \ + --optimizer.lr=2e-4 \ + --trainer.max_steps=5000 \ + --trainer.accelerator=gpu \ + --trainer.devices=1 \ + --trainer.accumulate_grad_batches=4 +``` + +You can also do this programmatically with the PyTorch Lightning `Trainer`: + +```python +from torch.optim import Adam + +from perceiver.data.text.wikitext import WikiTextDataModule, Task +from perceiver.model.text.clm import LitCausalLanguageModel, CausalLanguageModelConfig + +import pytorch_lightning as pl + + +# Lightning WikiText data module +data = WikiTextDataModule( + tokenizer="deepmind/language-perceiver", + max_seq_len=4096, + batch_size=16, + task=Task.clm, +) + +# Language model configuration object +model_config = CausalLanguageModelConfig( + vocab_size=data.vocab_size, + max_seq_len=data.max_seq_len, + num_latents=512, + num_channels=512, + num_self_attention_layers=8, + cross_attention_dropout=0.5, +) + +def configure_optimizers(self): + return Adam(self.parameters(), lr=2e-4) + +# Associate optimizer factory with Lightning module (not predefined there) +setattr(LitCausalLanguageModel, "configure_optimizers", configure_optimizers), + +# Lightning module of language model (a Perceiver AR) +lit_model = LitCausalLanguageModel.create(model_config) + +# Instantiate Lightning Trainer +trainer = pl.Trainer(accelerator="gpu", devices=1, max_steps=5000, accumulate_grad_batches=4) + +# Train model (will also preprocess dataset if used for the first time) +trainer.fit(lit_model, datamodule=data) +``` + +If you instead want to use plain PyTorch (without PyTorch Lightning, except for data sources): + +```python +from perceiver.model.text.clm import CausalLanguageModel + +import torch.nn.functional as F +from torch.optim import Adam + +data = ... +data.prepare_data() +data.setup() + +model_config = ... + +# Plain PyTorch module of language model +model = CausalLanguageModel(config=model_config) +model.train() + +optim = Adam(model.parameters(), lr=2e-4) + +# Simplified training loop compared to previous examples +# (no gradient accumulation, epochs instead of max_steps, ...) +for epoch in range(4): + for labels_ids, input_ids, _ in data.train_dataloader(): + logits = model(input_ids) + loss = F.cross_entropy(logits.permute(0, 2, 1), labels_ids[:, -model_config.num_latents:]) + loss.backward() + optim.step() + optim.zero_grad() +``` + +### Inference + +```python +from perceiver.model.text.clm import LitCausalLanguageModel + +data = ... + +# Load Lightning module from training checkpoint +lit_model = LitCausalLanguageModel.load_from_checkpoint("/path/to/checkpoint") + +# Obtain trained plain PyTorch model +model = lit_model.model.eval() + +# Get text preprocessor from data module +preproc = data.text_preprocessor() + +# Tokenize a sample prompt +prompt, _ = preproc.preprocess("A man was reading a book on a sunny day until he sudden") + +# Generate tokens from prompt via top-k sampling where k = f(vocab_size, threshold) +generated = model.generate(num=512, prompt=prompt[None, ...], threshold=0.9)[0] + +# Decode generated tokens +generated_text = data.tokenizer.decode(generated) +``` + +You can also run text generation interactively in the [Colab notebook](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/notebooks/inference_examples.ipynb). + +## Other implementations + +- [Perceiver](https://paperswithcode.com/paper/perceiver-general-perception-with-iterative#code) +- [Perceiver IO](https://paperswithcode.com/paper/perceiver-io-a-general-architecture-for#code) +- [Perceiver AR](https://paperswithcode.com/paper/general-purpose-long-context-autoregressive#code) diff --git a/docs/building-blocks.md b/docs/building-blocks.md index 3a7858d..5ac0b77 100644 --- a/docs/building-blocks.md +++ b/docs/building-blocks.md @@ -9,7 +9,7 @@ of this library. Core modules are the building blocks for [model construction](m Perceiver IO models are constructed from generic `PerceiverEncoder` and `PerceiverDecoder` classes and task-specific `InputAdapter` and `OutputAdapter` subclasses. Array dimensions (`M`, `C`), (`N`, `D`), (`O`, `F`) and (`O`, `E`) -have the following names in code and/or on the command line (see also code comments [here](model-construction.md#pytorch-model-api)): +have the following names in code and/or on the command line (see also code comments [here](model-construction.md#perceiver-io)): | Array dimension | Configuration parameter name | |-----------------|---------------------------------------------------------------------------------| @@ -46,3 +46,12 @@ always share their weights. Sharing the weights with the first cross-attention l `first_cross_attention_layer_shared`, sharing the weights with the first self-attention block can be configured with `first_self_attention_block_shared`. The default values of these configuration parameters are consistent with the Perceiver IO architecture (1 cross-attention layer, `L` self-attention blocks with weight sharing). + +## Perceiver AR + +![architecture](images/perceiver-ar.png) + +The implementation of [Perceiver AR](https://arxiv.org/abs/2202.07765) is very similar to a Perceiver IO encoder. +Perceiver AR additionally uses [rotary position embeddings](https://arxiv.org/abs/2104.09864) and uses a causal +cross- and self- attention mask. The current implementation is still experimental and a final implementation may +be entirely based on Perceiver IO. diff --git a/docs/dataset-preproc.md b/docs/dataset-preproc.md index cb1f71f..4c1012c 100644 --- a/docs/dataset-preproc.md +++ b/docs/dataset-preproc.md @@ -38,18 +38,18 @@ whatever you need for model training. --add_special_tokens=true ``` -- [wikitext](https://huggingface.co/datasets/wikitext) (`wikitext-103-raw-v1`), used for small-scale [training examples](../README.md#training-examples): +- [wikitext](https://huggingface.co/datasets/wikitext) (`wikitext-103-raw-v1`), used for [training examples](training-examples.md): ```shell python -m perceiver.scripts.text.preproc wikitext \ --tokenizer=bert-base-uncased \ --max_seq_len=512 \ - --add_special_tokens=true \ + --add_special_tokens=false \ --filter_empty=true \ --filter_headers=true ``` -- [imdb](https://huggingface.co/datasets/imdb) (`plain_text`), used for small-scale [training examples](../README.md#training-examples): +- [imdb](https://huggingface.co/datasets/imdb) (`plain_text`), used for [training examples](training-examples.md): ```shell python -m perceiver.scripts.text.preproc imdb \ @@ -58,6 +58,15 @@ whatever you need for model training. --add_special_tokens=true ``` +- [enwik8](https://huggingface.co/datasets/enwik8) (`enwik8`), used for [training examples](training-examples.md): + + ```shell + python -m perceiver.scripts.text.preproc enwik8 \ + --tokenizer=deepmind/language-perceiver \ + --max_seq_len=4096 \ + --add_special_tokens=false + ``` + ## Image datasets - [imagenet](https://huggingface.co/datasets/imagenet-1k): diff --git a/docs/docker-image.md b/docs/docker-image.md index 5956e92..c627b10 100644 --- a/docs/docker-image.md +++ b/docs/docker-image.md @@ -27,7 +27,7 @@ sudo docker run \ --name=perceiver-io \ --runtime=nvidia \ ghcr.io/krasserm/perceiver-io:latest \ - python -m perceiver.scripts.text.lm fit \ + python -m 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zf2}+31rY*^+@r{F*Sa^^Mr?mM9}U!nfULCTHvf8N#ytaUg@5aCF3BgxIz0F6ZdYVp z2*L+u<4gy(#VSC-jf%dCb-I!S(%!+c_(+Qon^MeJC!pjRJEo?OX@}xAwd($Vv9sv~ z7O0MZ@|drl1_;=JW^MdGb_4H20$UaB({nqu%lA|*RJZdpB|41gjkEwAlpT>vcZAlA*h#%#0;Nj zQOc-cU>L<`;*p<4VvBb5m40f=4?#gY{RSY_#WSD?r0@RyCdXCK{&3*n5!drlEqum0 zMR#4+nGf}l$+@Ywujtn&KnAg4mo0Zl!$y6m None: + super().add_arguments_to_parser(parser) + parser.link_arguments("data.max_seq_len", "model.max_seq_len", apply_on="instantiate") + parser.link_arguments("data.vocab_size", "model.vocab_size", apply_on="instantiate") + parser.set_defaults( + { + "model.num_latents": 512, + "model.num_channels": 512, + "model.num_self_attention_layers": 8, + "model.cross_attention_dropout": 0.5, + "model.post_attention_dropout": 0.0, + } + ) + + +if __name__ == "__main__": + CLI(LitCausalLanguageModel) +``` + +Training a `LitCausalLanguageModel` from scratch with the WikTtext-103-raw dataset can then be started with e.g.: + +```shell +python clm.py fit \ + --model.cross_attention_dropout=0.6 \ + --data=WikiTextDataModule \ + --data.task=clm \ + --data.tokenizer=deepmind/language-perceiver \ + --data.max_seq_len=4096 \ + --data.batch_size=24 \ + --optimizer=Adam \ + --optimizer.lr=2e-4 \ + --trainer.accelerator=gpu \ + --trainer.devices=-1 \ + --trainer.logger=TensorBoardLogger \ + --trainer.logger.save_dir=logs \ + --trainer.logger.name=clm +``` diff --git a/docs/pretrained-models.md b/docs/pretrained-models.md index f1b8863..41c6bcb 100644 --- a/docs/pretrained-models.md +++ b/docs/pretrained-models.md @@ -1,41 +1,41 @@ # Pretrained models -Parameters of pretrained models can be imported from the 🤗 [Hub](https://huggingface.co/models) as described in the -following subsections. Checkpoints from [Training examples](training-examples.md) are available too (follow the -link for further details). +Parameters of some pretrained Perceiver IO models can be imported from the 🤗 [Hub](https://huggingface.co/models) as +described in the following subsections. Checkpoints from [Training examples](training-examples.md) are available too +(follow the link for further details). ## Language model -Perceiver IO language model (UTF-8 bytes tokenization, vocabulary size of 262, 201M parameters) specified in Section 4 -(Table 1) and Appendix F (Table 11) of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795). See -[Model construction](model-construction.md) for further details. +Perceiver IO language model (UTF-8 bytes tokenization, vocabulary size of 262, 201M parameters) for masked language +modeling, as specified in Section 4 (Table 1) and Appendix F (Table 11) of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795): ```python from transformers import AutoConfig -from perceiver.model.text.language import convert_config, LanguageModel, LitLanguageModel +from perceiver.model.text.mlm import convert_config, LitMaskedLanguageModel, MaskedLanguageModel # Import and convert language model configuration from Huggingface Hub config = convert_config(AutoConfig.from_pretrained("deepmind/language-perceiver")) # Construct a PyTorch model and load pretrained parameters -model = LanguageModel(config) +model = MaskedLanguageModel(config) # Alternatively, construct a PyTorch Lightning module and load pretrained parameters -lit_model = LitLanguageModel.create(config) +lit_model = LitMaskedLanguageModel.create(config) ``` -On the command line, the pretrained model can be loaded with the `--model.params=deepmind/language-perceiver` option. +See [Model construction](model-construction.md) for further details. On the command line, the pretrained model can be +referenced with the `--model.params=deepmind/language-perceiver` option. ```shell -python -m perceiver.scripts.text.lm fit \ +python -m perceiver.scripts.text.mlm fit \ --model.params=deepmind/language-perceiver \ ... ``` ## Image classifier -The Perceiver IO image classifier (config A, 2D Fourier features, 48.8M parameters) specified in Appendix A of the -[Perceiver IO paper](https://arxiv.org/abs/2107.14795). +Perceiver IO ImageNet classifier (config A, 2D Fourier features, 48.8M parameters), as specified in Appendix A of the +[Perceiver IO paper](https://arxiv.org/abs/2107.14795): ```python from transformers import AutoConfig @@ -51,7 +51,7 @@ model = ImageClassifier(config) lit_model = LitImageClassifier.create(config) ``` -On the command line, the pretrained model can be loaded with the `--model.params=deepmind/vision-perceiver-fourier` +On the command line, the pretrained model can be referenced with the `--model.params=deepmind/vision-perceiver-fourier` option. ```shell diff --git a/docs/training-examples.md b/docs/training-examples.md index 3b9dc68..845986f 100644 --- a/docs/training-examples.md +++ b/docs/training-examples.md @@ -1,19 +1,19 @@ # Training examples -Here are some command line examples how to train Perceiver IO models with this library. If a model must be initialized -with parameters from a previous run, it references a checkpoint from that run with the `--model.params` option. You can -download these checkpoints [here](https://martin-krasser.com/perceiver/logs-update-7.zip) if you don't want to run all -examples yourself. Training results are used in [Inference examples](../notebooks/inference_examples.ipynb) -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/krasserm/perceiver-io/blob/main/notebooks/inference_examples.ipynb) +This section contains command line examples for training [Perceiver IO](#perceiver-io) and [Perceiver AR](#perceiver-ar) +models. If a model must be initialized with parameters from a previous run, it references a checkpoint from that run +with the `--model.params` option. Checkpoints for all command line examples can be downloaded [here](https://martin-krasser.com/perceiver/logs-update-8.zip). +They are also used in [Inference examples](../notebooks/inference_examples.ipynb). -These examples were tested on a machine with 4x RTX 3080ti GPUs (12 GB memory each). You'll need to adjust some +The examples were tested on a machine with 4x RTX 3080ti GPUs (12 GB memory each). You'll need to adjust some settings (batch size, ...) for running them on a different hardware configuration. Furthermore, I didn't really tune these examples, so you'll likely get better results with a bit of experimentation. ## Dataset preprocessing Although data modules automatically download and preprocess datasets if needed, it is usually faster if you preprocess -datasets prior to training (see [Dataset preprocessing](dataset-preproc.md) for details): +datasets prior to training (see [Dataset preprocessing](dataset-preproc.md) for details). Running the following commands +is optional: ```shell python -m perceiver.scripts.text.preproc imdb \ @@ -24,25 +24,34 @@ python -m perceiver.scripts.text.preproc imdb \ python -m perceiver.scripts.text.preproc wikitext \ --tokenizer=bert-base-uncased \ --max_seq_len=128 \ - --add_special_tokens=true \ --filter_empty=true \ - --filter_headers=true + --filter_headers=true \ + --task=mlm + +python -m perceiver.scripts.text.preproc wikitext \ + --tokenizer=deepmind/language-perceiver \ + --max_seq_len=4096 \ + --filter_empty=false \ + --filter_headers=false \ + --task=clm ``` -## Language model fine-tuning +## Perceiver IO + +### Language model fine-tuning (MLM) -Fine-tune a pretrained `deepmind/language-perceiver` model with masked language modeling and whole word masking on -the IMDb dataset (*unsupervised* split). It prepares the language model for a better performance on IMDb [sentiment +Fine-tune a pretrained `deepmind/language-perceiver` model with masked language modeling (MLM) and whole word masking +on the IMDb dataset (*unsupervised* split). It prepares the language model for a better performance on IMDb [sentiment classification](#sentiment-classification). The tokenizer is a UTF-8 bytes tokenizer and the model attends to the raw bytes of the input. Word masking is done dynamically at data loading time i.e. each epoch has a different set of words masked. ```shell -python -m perceiver.scripts.text.lm fit \ +python -m perceiver.scripts.text.mlm fit \ --model.params=deepmind/language-perceiver \ --model.activation_checkpointing=true \ --data=ImdbDataModule \ - --data.target_task=mlm \ + --data.task=mlm \ --data.tokenizer=deepmind/language-perceiver \ --data.add_special_tokens=true \ --data.max_seq_len=2048 \ @@ -62,12 +71,13 @@ python -m perceiver.scripts.text.lm fit \ --trainer.logger.name=mlm ``` -## Sentiment classification +### Sentiment classification Train a text classification model on the IMDb dataset (*train* split). The encoder of the classifier is the fine-tuned -language model encoder from the [previous run](#language-model-fine-tuning) (`--model.encoder.params=...`), the decoder -is a randomly initialized classification decoder (see `TextClassifier` and `LitTextClassifier` in [classifier.py](../perceiver/model/text/classifier.py)). -First, only the decoder is trained, the encoder is frozen (`--model.encoder.freeze=true`) +language model encoder from the [previous run](#language-model-fine-tuning-mlm) (`--model.encoder.params=...`), the +decoder is a randomly initialized classification decoder (see `TextClassifier` and `LitTextClassifier` in +[classifier.py](../perceiver/model/text/classifier.py)). First, only the decoder is trained, the encoder is frozen +(`--model.encoder.freeze=true`) ```shell python -m perceiver.scripts.text.classifier fit \ @@ -76,7 +86,7 @@ python -m perceiver.scripts.text.classifier fit \ --model.encoder.dropout=0.0 \ --model.decoder.dropout=0.1 \ --data=ImdbDataModule \ - --data.target_task=clf \ + --data.task=clf \ --data.tokenizer=deepmind/language-perceiver \ --data.add_special_tokens=true \ --data.max_seq_len=2048 \ @@ -104,7 +114,7 @@ python -m perceiver.scripts.text.classifier fit \ --model.encoder.dropout=0.1 \ --model.decoder.dropout=0.1 \ --data=ImdbDataModule \ - --data.target_task=clf \ + --data.task=clf \ --data.tokenizer=deepmind/language-perceiver \ --data.add_special_tokens=true \ --data.max_seq_len=2048 \ @@ -163,31 +173,27 @@ python -m perceiver.scripts.text.classifier validate \ When training only the classification decoder, the validation accuracy is 91.6%. Fine-tuning encoder and decoder on the classification task further increases validation accuracy to 94.4%. -## Language model pretraining +### Language model pretraining (MLM) Pretrain a smaller language model (45.2M parameters) with masked language modeling and whole word masking on the -Wikitext-103 dataset. This is a toy example for demonstrating how to use a custom model configuration/architecture -and another 🤗 tokenizer (`bert-base-uncased`, a SentencePiece tokenizer with a vocabulary of size of 30,522). To -speed up training, `--data.max_seq_len=128` and `--model.num_latents=64` is used (a quarter of the default values). +Wikitext-103 dataset. The example uses a custom model configuration/architecture and another 🤗 tokenizer +(`bert-base-uncased`, a SentencePiece tokenizer with a vocabulary of size of 30,522). To speed up training, +`--data.max_seq_len=128` and `--model.num_latents=64` is used (a quarter of the default values). ```shell -python -m perceiver.scripts.text.lm fit \ +python -m perceiver.scripts.text.mlm fit \ --model.activation_checkpointing=true \ --model.num_latents=64 \ --model.num_latent_channels=768 \ --model.encoder.num_input_channels=512 \ - --model.encoder.num_cross_attention_v_channels=768 \ - --model.encoder.num_self_attention_v_channels=768 \ --model.encoder.num_self_attention_layers_per_block=6 \ - --model.encoder.cross_attention_widening_factor=2 \ - --model.encoder.self_attention_widening_factor=2 \ - --model.encoder.dropout=0.0 \ - --model.decoder.num_cross_attention_v_channels=512 \ - --model.decoder.cross_attention_widening_factor=2 \ - --model.decoder.dropout=0.0 \ + --model.encoder.cross_attention_widening_factor=4 \ + --model.encoder.self_attention_widening_factor=4 \ + --model.encoder.dropout=0.1 \ + --model.decoder.cross_attention_widening_factor=4 \ + --model.decoder.dropout=0.1 \ --data=WikiTextDataModule \ --data.tokenizer=bert-base-uncased \ - --data.add_special_tokens=true \ --data.filter_empty=true \ --data.filter_headers=true \ --data.max_seq_len=128 \ @@ -200,8 +206,6 @@ python -m perceiver.scripts.text.lm fit \ --trainer.accelerator=gpu \ --trainer.precision=16 \ --trainer.devices=4 \ - --trainer.strategy=ddp_sharded \ - --trainer.accumulate_grad_batches=2 \ --trainer.val_check_interval=0.5 \ --trainer.log_every_n_steps=20 \ --trainer.logger=TensorBoardLogger \ @@ -209,7 +213,7 @@ python -m perceiver.scripts.text.lm fit \ --trainer.logger.name=mlm_pre ``` -## Image classification +### Image classification Train a tiny image classifier (805K parameters) on the MNIST dataset. The model attends to individual pixels of the input image and uses Fourier position encodings. This is another toy example that demonstrates how to use a custom @@ -258,3 +262,42 @@ python -m perceiver.scripts.image.classifier validate \ val_loss 0.06774937361478806 ────────────────────────────────────────────────── ``` + +## Perceiver AR + +### Language model pretraining (CLM) + +Pretrain a smaller language model (30.7M parameters) with causal language modeling on the WikiText-103-raw dataset. The +tokenizer is a UTF-8 bytes tokenizer and the model attends to the raw bytes of the input. + +```shell +python -m perceiver.scripts.text.clm fit \ + --model.num_latents=512 \ + --model.cross_attention_dropout=0.5 \ + --model.post_attention_dropout=0.0 \ + --data=WikiTextDataModule \ + --data.tokenizer=deepmind/language-perceiver \ + --data.max_seq_len=4096 \ + --data.batch_size=24 \ + --data.num_workers=3 \ + --data.task=clm \ + --optimizer=Adam \ + --optimizer.lr=2e-4 \ + --trainer.max_steps=8000 \ + --trainer.accelerator=gpu \ + --trainer.devices=2 \ + --trainer.val_check_interval=0.5 \ + --trainer.gradient_clip_val=0.5 \ + --trainer.accumulate_grad_batches=2 \ + --trainer.logger=TensorBoardLogger \ + --trainer.logger.save_dir=logs \ + --trainer.logger.name=clm_pre +``` + +For better generalization to shorter sequences I found random sequence truncation helpful which can be enabled with +`--model.random_sequence_trucation=true`. Random sequence truncation randomly truncates sequences in a batch to a +length `randint(16, n+1)` where `n` is the original sequence length. + +With option `--model.validation_sample_record=-1` a sequence is randomly picked from the validation set and used as +prompt for sequence generation during validation. The prompt and the generated sequence is logged to Tensorboard. You +can also use option `--model.validation_sample_prompt="My own sample prompt"` to provide your own prompt. diff --git a/notebooks/inference_examples.ipynb b/notebooks/inference_examples.ipynb index 5e6df2c..f63a927 100644 --- a/notebooks/inference_examples.ipynb +++ b/notebooks/inference_examples.ipynb @@ -29,8 +29,9 @@ }, "outputs": [], "source": [ - "!pip install perceiver-io[image,text]==0.5.1\n", + "!pip install perceiver-io[image,text]==0.6.0\n", "!pip install matplotlib\n", + "!pip install termcolor\n", "!pip install \"ipywidgets>=7,<8\"" ] }, @@ -46,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "27b64a65", "metadata": { "id": "27b64a65" @@ -67,26 +68,26 @@ }, { "cell_type": "markdown", - "source": [ - "Add support for external widgets:" - ], + "id": "8u3wrorgB1io", "metadata": { "id": "8u3wrorgB1io" }, - "id": "8u3wrorgB1io" + "source": [ + "Add support for external widgets:" + ] }, { "cell_type": "code", - "source": [ - "from google.colab import output\n", - "output.enable_custom_widget_manager()" - ], + "execution_count": 3, + "id": "iveRLBF6By0A", "metadata": { "id": "iveRLBF6By0A" }, - "id": "iveRLBF6By0A", - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "from google.colab import output\n", + "output.enable_custom_widget_manager()" + ] }, { "cell_type": "markdown", @@ -97,45 +98,55 @@ "source": [ "# Inference examples\n", "\n", - "This notebook demonstrates how to use Perceiver IO models from the [perceiver-io](https://github.com/krasserm/perceiver-io) library. Both, pretrained models and models trained in section [Training examples](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md) are used. The latter requires some checkpoints to be downloaded:" + "This notebook demonstrates how to use Perceiver IO and Perceiver AR models from the [perceiver-io](https://github.com/krasserm/perceiver-io) library. Both, pretrained models and models trained in section [Training examples](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md) are used. The latter requires some checkpoints to be downloaded:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "19f69e83", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "19f69e83", - "outputId": "e78ffaae-041f-4ab9-9aef-b2e4e4588b47" + "outputId": "1b4f4811-8ff4-4758-a895-842343420eb5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "--2022-08-31 09:09:16-- https://martin-krasser.com/perceiver/logs-update-7.zip\n", + "--2022-09-25 10:09:28-- https://martin-krasser.com/perceiver/logs-update-8.zip\n", "Resolving martin-krasser.com (martin-krasser.com)... 217.160.0.142, 2001:8d8:100f:f000::209\n", "Connecting to martin-krasser.com (martin-krasser.com)|217.160.0.142|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", - "Length: 2406684079 (2.2G) [application/zip]\n", + "Length: 2721010396 (2.5G) [application/zip]\n", "Saving to: ‘logs.zip’\n", "\n", - "logs.zip 100%[===================>] 2.24G 28.7MB/s in 86s \n", + "logs.zip 100%[===================>] 2.53G 14.5MB/s in 3m 4s \n", "\n", - "2022-08-31 09:10:43 (26.7 MB/s) - ‘logs.zip’ saved [2406684079/2406684079]\n", + "2022-09-25 10:12:34 (14.1 MB/s) - ‘logs.zip’ saved [2721010396/2721010396]\n", "\n" ] } ], "source": [ "# Download checkpoints\n", - "!wget -nc -O logs.zip https://martin-krasser.com/perceiver/logs-update-7.zip\n", + "!wget -nc -O logs.zip https://martin-krasser.com/perceiver/logs-update-8.zip\n", "!unzip -qo logs.zip" ] }, + { + "cell_type": "markdown", + "id": "2b953bde", + "metadata": { + "id": "2b953bde" + }, + "source": [ + "## Perceiver IO" + ] + }, { "cell_type": "markdown", "id": "1253bb24", @@ -143,9 +154,9 @@ "id": "1253bb24" }, "source": [ - "## Masked language modeling\n", + "### Masked language modeling\n", "\n", - "We'll use a pretrained and a fine-tuned language model, trained with masked language modeling (MLM) and whole word masking. The model is the *Perceiver IO* language model specified in Appendix F.2 of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795) (UTF-8 bytes tokenization, vocabulary size of 262, 201M parameters). MLM pretraining is described in section Appendix F.3. Fine-tuning on IMDb is described in [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#language-model-fine-tuning).\n", + "We'll use a pretrained and a fine-tuned language model, trained with masked language modeling (MLM) and whole word masking. The model is the *Perceiver IO* language model specified in Appendix F.2 of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795) (UTF-8 bytes tokenization, vocabulary size of 262, 201M parameters). MLM pretraining is described in section Appendix F.3. Fine-tuning on IMDb is described in [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#language-model-fine-tuning-mlm).\n", "\n", "This section demonstrates how the pretrained and the fine-tuned model fill `[MASK]` tokens in sample text. The tokenizer is a UTF-8 bytes tokenizer and the models attend to the raw bytes generated by the tokenizer. Therefore,\n", "each `[MASK]` token masks a single byte:" @@ -153,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "0bb45997", "metadata": { "id": "0bb45997" @@ -178,44 +189,44 @@ "id": "3246fc42" }, "source": [ - "The tokenizer is a Huggingface tokenizer identified by `deepmind/language-perceiver`. It is loaded and used by the `TextPreprocessor` class that prepares text for being processed by NLP models. A `MaskedSamplePredictionUtil` implements the boilerplate of mask filling." + "The tokenizer is a Huggingface tokenizer identified by `deepmind/language-perceiver`. It is loaded and used by the `TextPreprocessor` class that prepares text for being processed by NLP models. A `MaskedSampleFiller` implements the boilerplate of mask filling." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9ba9ba48", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ - "81891bda64b54c58b04a432e8279728f", - "d6e946a6c12f481fa0c89d0debeefed3", - "17e2575a80854df89c35dce84c3acac3", - "984445e2971d447186a430eabdb3fd0d", - "bb929f5e5bd6409883e9ce0200318fd8", - "9e92cb13e6ae498ba5aa73ed25e98ab8", - "9e84c922df964859a6f778ff6aaf4d63", - "f20d182226124889bb180eb6204a3ca6", - "95e2813fa2eb4b78863657421bf5bab9", - "f0517cb75c0645f294018890f997f038", - "f3b54c004f76420c91db456243c46787", - "4f28fa590d594b809701c05e080a5dba", - "93d9b5c27bef442885f948e2f2fa8a77", - "b250e789ff134c40ad39a255ce508614", - "13d2940521584ecca7a5a48538ac6c64", - "6faf7081ada34ef691c7e57b60e0174a", - "db2ddf1fe46240cfac99a5c57c8fc718", - "b77fdb4f2e0947ceb2091a63dc4359fd", - "6a369bb79ec54afaa3248ad84fc8feb6", - "a4a9e81379ee47b7b393f35fa06270ed", - "97e5e985af604bd4b71802f4298e1d73", - "f1971d541b944569a4be7a25605a1615" + "d25a1c2ce70846c1aae777a24fcbea20", + "1313867af46a40f69a3e5c5598d14c59", + "09b2c97af257490895f6216db74fa1cc", + "c4388aa1bb4f4bccb56a93a8bba43a1f", + "c9cd999999da40eba9b2494cd9e423b8", + "405be233085d48be9ae518b7cea5cafc", + "d51ef5e175eb4e6c9bfa60070945f094", + "a6a4bcf737b2497dad983daa5ba7186e", + "0886212067324a3fb960503406c99afa", + "2a320a2aab8d402aa8c58eb7e344cba3", + "3048fc9d0dd8457d9d659e4793a07bc1", + "8438e57769954f2493bce306023a5baf", + "8cd0ebaae8ce482eb2d726f3fa10d1f2", + "856770f3ab854d37a2f4afd9fd548b16", + "cb8fbe0f3b114de686ae3ae59b1f8f2f", + "1bc5639d1a72496a934c192b48ab1389", + "113168f74ccd4232b583501dcc98aacd", + "ad5ddfaa12a94c9f94e361fdfeb2cafd", + "46cfd893e17a4af5b718fc4c7b37e8a0", + "1bdc76699bb14a6c9df8e3b7d21b02ac", + "49ba7f28b0fb47f1aa9326e6e6aa2dca", + "3933be68a8b44c9e9063467e88e75c73" ] }, "id": "9ba9ba48", - "outputId": "a936505b-802b-4d83-e8fb-dbf9a809f2b4" + "outputId": "12abcee3-44e2-443c-e4ae-af73c3f82ffc" }, "outputs": [ { @@ -227,7 +238,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "81891bda64b54c58b04a432e8279728f" + "model_id": "d25a1c2ce70846c1aae777a24fcbea20" } }, "metadata": { @@ -249,7 +260,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f1971d541b944569a4be7a25605a1615" + "model_id": "3933be68a8b44c9e9063467e88e75c73" } }, "metadata": { @@ -265,15 +276,15 @@ ], "source": [ "from perceiver.data.text import TextPreprocessor\n", - "from perceiver.model.text.utils import MaskedSamplePredictionUtil\n", + "from perceiver.model.text.mlm import MaskedSampleFiller\n", "\n", "preproc = TextPreprocessor(tokenizer=\"deepmind/language-perceiver\", max_seq_len=2048, add_special_tokens=True)\n", - "util = MaskedSamplePredictionUtil(preproc)" + "filler = MaskedSampleFiller(preproc)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "041fd106", "metadata": { "id": "041fd106" @@ -296,46 +307,46 @@ "id": "22125f5b" }, "source": [ - "### Pretrained model\n", + "#### Pretrained model\n", "\n", "The pretrained language model is initialized with parameters downloaded from the Huggingface Hub." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "ec32006b", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ - "8cb60be6d5bf4e3e8a21646112b192c7", - "790d0264021845489079f97840d6d7c0", - "53b4c588a4ba4f8cbe69f0abd3a930e8", - "2f88b7076925485687b1b270946e150d", - "31aaee19ae6245a8b0971e0c14b01a41", - "e406019edf364c6abb8bac120a6e57c8", - "87eab324c01447d3919ebaf24be52140", - "ede6089a39c449dca3f994ca591454bf", - "83d8fb241a28435b9cb74465e97b43de", - "a52d3625d4294df5ad6efacddeb7c12e", - "8a178c580ea84571a40c3f8a722d1b9e", - "94544d7da5924fcf9528cadcf6a3818a", - "fbcc26ff26154921bd9c5b1a0242c674", - "112e23c85d374aad8b2d6a98e8efb431", - "829c6deb5e784f8f9546ee201482123c", - "90b7afa88c044797954d99de935c80e5", - "3b28dc755c45438e986f4dc403eef384", - "0219de347fad4343a8b436525064d50f", - "e21b0c614e9d40668c381818e0cbfac1", - "25a728fb49114478b78bdc74670752ee", - "03cda6262df4428c8266e1fdd2274987", - "bc36c225ffc145a19376d8ea41a73670" + "2b5ced32af63400f95f48e352ff26ece", + "73e3c5ad91f4452baccc928fb6b2b70e", + "a53eb1f028d348e68a8fe7ad32caa323", + "92b72b303b75447dae3f576638f1102b", + "e1f44519f9c048edb2ab8e6969d24a72", + "7ce35f9e3acc4fdf8181548c5618dd36", + "5630c8f68e684d7d827c224eba4b913b", + "b185da862d524d8ab59ef499c1e02850", + "b600a5c2e463457c9ddbb7aba3ee3fc8", + "ee8af1bb432d49c0b45f77355450f1d1", + "37959a2e5bad4e358e5e3958d0caccf8", + "3165d63cd8fb48bf8dc1fd7e60071c2e", + "d583a95035154d1b9814a7f3935d5089", + "1efdf697f2ee4d048ab755b70aa8cb13", + "9843286691fc40d481bdfe107c1d3bbc", + "6af6708d574f42b3944460c9c7311eca", + "6ffeeeafb94f48f18075d01c6c2dca2e", + "ce53f3db28bc44c0bef2278863ad4bed", + "ec7ff29f1f314467bba5fc68c770a076", + "07e264c93c524b32a2063bb32a7ec50e", + "abd8b0afc56d49f98fe5abea82ec0778", + "b23819d984574cef89164b209542bea8" ] }, "id": "ec32006b", - "outputId": "1751a88a-bc0a-4594-a0fc-efb0c541ce0a" + "outputId": "d1b3306e-40ec-4be9-a4ae-4032f7d5b628" }, "outputs": [ { @@ -347,7 +358,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "8cb60be6d5bf4e3e8a21646112b192c7" + "model_id": "2b5ced32af63400f95f48e352ff26ece" } }, "metadata": { @@ -369,7 +380,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "bc36c225ffc145a19376d8ea41a73670" + "model_id": "b23819d984574cef89164b209542bea8" } }, "metadata": { @@ -384,17 +395,17 @@ } ], "source": [ - "from perceiver.model.text.language import convert_config, LanguageModel\n", + "from perceiver.model.text.mlm import convert_config, MaskedLanguageModel\n", "\n", "# Load model configuration from the Huggingface Hub.\n", "config = AutoConfig.from_pretrained(\"deepmind/language-perceiver\")\n", "\n", "# Convert the configuration, instantiate the language \n", "# model and import the pretrained model parameters.\n", - "model = LanguageModel(convert_config(config))\n", + "model = MaskedLanguageModel(convert_config(config))\n", "\n", "# Configure the prediction utility to use this model.\n", - "util.model = model.eval()" + "filler.model = model.eval()" ] }, { @@ -409,14 +420,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "5559385d", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5559385d", - "outputId": "6a8f3a1b-58ec-4a43-f41b-22ae263e9c4f" + "outputId": "e0fbc77b-901e-469f-bc19-2d18f728fc4a" }, "outputs": [ { @@ -448,7 +459,7 @@ } ], "source": [ - "masked_samples, filled_samples = util.fill_masks(masked_samples, num_predictions=1)\n", + "masked_samples, filled_samples = filler.fill(masked_samples, num_predictions=1)\n", "print_predictions(masked_samples, filled_samples)" ] }, @@ -469,7 +480,7 @@ "id": "a970361f" }, "source": [ - "### Fine-tuned model" + "#### Fine-tuned model" ] }, { @@ -484,22 +495,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e1c96907", "metadata": { "id": "e1c96907" }, "outputs": [], "source": [ - "from perceiver.model.text.language import LitLanguageModel\n", + "from perceiver.model.text.mlm import LitMaskedLanguageModel\n", "\n", "ckpt = \"logs/mlm/version_0/checkpoints/epoch=009-val_loss=1.174.ckpt\"\n", "\n", "# Load the PyTorch Lightning module of the language model from a checkpoint\n", - "lit_model = LitLanguageModel.load_from_checkpoint(ckpt, params=None)\n", + "lit_model = LitMaskedLanguageModel.load_from_checkpoint(ckpt, params=None)\n", "\n", "# Update the prediction utility to use the wrapped PyTorch language model.\n", - "util.model = lit_model.model.eval()" + "filler.model = lit_model.model.eval()" ] }, { @@ -514,14 +525,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6f17ea70", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6f17ea70", - "outputId": "e7eafac0-721c-4662-879d-20b3a656c71b" + "outputId": "e859b899-2d92-4373-d8b3-667700dae728" }, "outputs": [ { @@ -553,7 +564,7 @@ } ], "source": [ - "masked_samples, filled_samples = util.fill_masks(masked_samples, num_predictions=1)\n", + "masked_samples, filled_samples = filler.fill(masked_samples, num_predictions=1)\n", "print_predictions(masked_samples, filled_samples)" ] }, @@ -574,16 +585,16 @@ "id": "866d4729" }, "source": [ - "## Sentiment classification\n", + "### Sentiment classification\n", "\n", - "The sentiment classification model used in this section is a custom Perceiver IO text classification model that was trained to predict the sentiment of IMDb reviews (*positive* or *negative*). It uses the encoder from [masked language modeling](#masked-language-modeling) and a classification decoder for the binary classification task. Training details are described [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#sentiment-classification). \n", + "The sentiment classification model used in this section is a custom Perceiver IO text classification model that was trained to predict the sentiment of IMDb reviews (*positive* or *negative*). It uses the encoder from the previous section and a classification decoder for the binary classification task. Training details are described [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#sentiment-classification). \n", "\n", "The classification model is loaded from a training checkpoint:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "fca9c663", "metadata": { "id": "fca9c663" @@ -608,19 +619,19 @@ "id": "a73bb183" }, "source": [ - "We can reuse the `TextPreprocessor` from [masked language modeling](#masked-language-modeling) to feed some mini-reviews through the model and predict their sentiment." + "We can reuse the `TextPreprocessor` from the previous section to feed some mini-reviews through the model and predict their sentiment." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c73065af", "metadata": { - "id": "c73065af", "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "cf510f91-267a-486b-d7ca-9717f55adec9" + "id": "c73065af", + "outputId": "2844c56d-f9e5-41aa-b9eb-f2da15bbe2bd" }, "outputs": [ { @@ -656,50 +667,50 @@ "id": "da71afeb" }, "source": [ - "## Image classification\n", + "### Image classification\n", "\n", "This section demonstrates how Perceiver IO vision models can be used to predict the class label of an input image. Both models attend to the individual pixels of an input image and use Fourier position encodings.\n", "\n", - "### ImageNet classifier\n", + "#### ImageNet classifier\n", "\n", "The ImageNet classifier is specified in Appendix A of the [Perceiver IO paper](https://arxiv.org/abs/2107.14795) (config A, 2D Fourier features, 48.8M parameters). It is initialized with pretrained parameters downloaded from the Huggingface Hub." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "c0ae8bdf", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 136, + "height": 116, "referenced_widgets": [ - "a1912870861b4d33853a8fdb9b376f97", - "db058a194ff849bdb47df6cf0b924250", - "ea8eb68a95f04be5a1a66b73d9e3e64e", - "d67d82fc7a7c44d4b4ebe6f9d53411ec", - "1a3a63f0d0584e488e475656021e5313", - "859623212bf54064ae276268b6d95150", - "92f270e494ca495e97a2063382424556", - "85e73ff7112e45d7986d19fd5dcfe18c", - "6f3d95bd8e8b471a9b7904e57ad37234", - "178d8f2846d6464f9ab22a634ff7957c", - "9a5bd4e79e2342c2bba395bd3af98ee9", - "494b645f9d484277823291a47f806236", - "39096d416bab4adda9d56324f1183414", - "5fec3c7357fc4dc587ed5ce1856f606f", - "a49587d1417b45f7baadda7baa99570a", - "c0041cd696654b678e04f71b58e131a9", - "e94b4b7150e94f5b9c63e0afeb3a8359", - "38c1a9fb8dd747bbaa7d6c739722db62", - "86af5a0916e14a5d83e92150ee106765", - "d1c0f261398a416f8ca28e44e0710cf3", - "5b6d0fc47ede416b8ad562722f2ccecb", - "5f5956ed197b4827a540559af5f245bb" + "36ae0908d6e0498fada2befe03bf7677", + "dd4b7fe8a44948cf8e3c4183d58b0826", + "5619aa4fcb524b5cb9b2b67228eaf358", + "a91772e5f76d45df949effb16fec262a", + "dd512f0798074952b26eed06672a8efa", + "318ee4e204454cb08697bed018c0477a", + "e69d865cd0f5401d8c70f92efa218c9d", + "eda1e3520bd644798d2ae3ccdc19ed42", + "67d84c621dfa4858b7f7e64727dfd6d3", + "1d2a85ff571a410eacbcfc76e35c8b8b", + "d0985e5bf18b4046a003077464bd7238", + "e31295e66a4547719a8aaa6408226c17", + "31fafd9f7ae7481a805b391680516de9", + "568d43ad99e34a7ba449702f8e5cb616", + "d5e25da8042a4fcba27e3e9ad3328697", + "822d9512311b4b8cb37283253efe6afd", + "088af905eb1e48c5a8d94e2bad086338", + "0ce78b2ddb974f129f3a714833d70758", + "b818fe44e53d4d5fab5263d7fb458209", + "d3872cc933e84313825275ce751f5eca", + "a38d632ea13546e4ac14fcea36f92f85", + "2e117bae59a64324bad810cee2e647af" ] }, "id": "c0ae8bdf", - "outputId": "36b17bd7-3086-4aa1-8b6d-fb5fcbac013b" + "outputId": "8671bd88-6274-4813-9ab4-4e887c98ede8" }, "outputs": [ { @@ -711,7 +722,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "a1912870861b4d33853a8fdb9b376f97" + "model_id": "36ae0908d6e0498fada2befe03bf7677" } }, "metadata": { @@ -741,7 +752,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5f5956ed197b4827a540559af5f245bb" + "model_id": "2e117bae59a64324bad810cee2e647af" } }, "metadata": { @@ -778,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "aa99d503", "metadata": { "colab": { @@ -786,14 +797,14 @@ "height": 657 }, "id": "aa99d503", - "outputId": "2ed1ea67-07a0-4a2d-f104-fa17ffb890c5" + "outputId": "04ecffc8-c482-47db-895d-67014be8b50f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "" + "" ], "image/png": 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\n" }, @@ -819,7 +830,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "c8a6463a", "metadata": { "id": "c8a6463a" @@ -844,14 +855,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "945d5db0", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "945d5db0", - "outputId": "03926dc2-853f-4f48-8884-fd44472879d0" + "outputId": "112e7658-04c3-4ed4-cf97-b653c7ff7b30" }, "outputs": [ { @@ -874,7 +885,7 @@ "id": "ed0c85a8" }, "source": [ - "### MNIST classifier\n", + "#### MNIST classifier\n", "\n", "The MNIST classifier is a tiny Perceiver IO model trained from scratch on the MNIST dataset as described [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#image-classification). \n", "\n", @@ -883,61 +894,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "b2a91be0", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 443, + "height": 423, "referenced_widgets": [ - "c5c16b9747b0495a9db083e0315dae92", - "cdd85677201345f8ac76089b5e2afcfd", - "825c670dbfff435d82dbd84efb652c68", - "fd23a0264ab64254bc8c492de9b7d877", - "d35bfc521a794772b4669a103753f632", - "28e87efa64e8483e9f7c38b888a5ac29", - "96130c47bfac4e268175860326e453bb", - "e515feb888044ca08979ca2951883cfb", - "ba53964cb26e43e4b1289f01d1eecec7", - "726f7b5c07f84917814dcdcb1b5e4971", - "329b9796d3c0411082cfb21bd6ff97eb", - "4a6531903c5b40449bdb0d79e2dcde52", - "b6a5e474e440485cbb72d9432fd9cbb9", - "b0ee913072a348658551395a7720c648", - "23033fe841344e36a53ea6c76a5a7c4e", - "41d8b74667a2485bbbe6b1dea6e99c02", - "3443f7b8593d4f5998fa5d8dca57fc9a", - "192e536426cf4b03b1200a3f35d205f0", - "55ff765e129742b497d76b42bf3098c5", - "a85bb623f2f44898a44368123e2df59b", - "877b1bc171044c6884a37611a713e2e2", - "08ab1d484a084b8c99535da15e9b952a", - "81c7e1cc11854ef9ad55d500b293e640", - "83381d6f8a7342d699e8371b2b324bcd", - 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"f5a3d0aef8024410b6dbc21ab8eba549", + "826e38e89260458998631217141c5a4c", + "6a5ea24e432f41d6b59c42298305bce9", + "6dfa741efe4545c3a7225f764e91947b", + "b745dd12baf64712a1d15170f642858e", + "1595f848835646e689bae86bbe2b22bf", + "ceb3e2b0c1e94039a2352d310b82c68f", + "2007319d073246b98efcf3b0aa850f02", + "cab2b6e56be64dd5ab1929efdae0cebb", + "eb5442385149473e9e7441d8fefe9719", + "0ad3ac4ba3b94135b305d47bc9000cf1", + "333cf1c9b1e1466aa8e533f7c209fdd3", + "57ab3385c3bb45aa836dcc97098548cb", + "05877d7d7e9649f8a4f49f537af12457", + "6e9ede411a5e43e3b18ead91da12c3f7", + "bef89a1111254fd5a2e0468e872a0845", + "6881a13e061b4a228170365b3fff8528", + "0e7876794d4941c3bbe7cf9813e99271", + "dfe6d74ba3344ea8abf7e3bb7ba9f7f7", + "dad3618c5ab1449d99fb1f38b34ec2bd", + "593c1f1f726a47bb8f993effceaaee54", + "86c7f0da30bb4a9a86bd1186a2410cc9", + "795392ece1fd4c709d98d7689e1fbc27", + "b41497d455d8401692cfee0dbad023f7", + "9eaa0049c91e407b92d821088e1a2703", + "31eec2678e844920af191f5b010cd347", + "8bdd14b415e94a899ae5eb4d18a278bd", + "f324152435ef4986b1e4bcddfd45421d", + "af613448775f43b7865cd81de6a3c143", + "793a39a5f85b454da4fb3d8c15c47de3", + "3b7a7df25913453fbd38c88b34966f50", + "84264e96814b419c8f287bdaff20734a", + "65f2842955c5459a95b7e56670ef0cd0", + "676aa7e9649e43c9b9863dffdacd6882", + "2ccd6cc868be46a0884ec9c81ba3cc3f", + "a2ca9205fe0c4f02b79a6de7cc101757", + "e3fc10667e1f4955914876213ecb8fe2", + "f2b190becab3434083251ab66c56ceda" ] }, "id": "b2a91be0", - "outputId": "9f053fdf-1ecf-4f56-80db-e6770eecf67d" + "outputId": "1a7ba60a-a31b-4ba3-b14c-7c50a7ed3ec4" }, "outputs": [ { @@ -957,7 +968,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c5c16b9747b0495a9db083e0315dae92" + "model_id": "b88a23e80f2342e0aa2c475e6532e589" } }, "metadata": { @@ -989,7 +1000,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "08ab1d484a084b8c99535da15e9b952a" + "model_id": "bef89a1111254fd5a2e0468e872a0845" } }, "metadata": { @@ -1021,7 +1032,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2937e2b4cff34e82b728070764d90d35" + "model_id": "8bdd14b415e94a899ae5eb4d18a278bd" } }, "metadata": { @@ -1053,7 +1064,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "01e693ede66a46f3974153dae1575468" + "model_id": "f2b190becab3434083251ab66c56ceda" } }, "metadata": { @@ -1095,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "e483ab23", "metadata": { "id": "e483ab23" @@ -1125,18 +1136,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "c5c6ed54", "metadata": { - "id": "c5c6ed54", - "pycharm": { - "name": "#%%\n" - }, "colab": { "base_uri": "https://localhost:8080/", "height": 482 }, - "outputId": "3496bba3-6423-4488-df09-376d96a033fa" + "id": "c5c6ed54", + "outputId": "52076cc0-f272-417b-8141-47472d3faf76", + "pycharm": { + "name": "#%%\n" + } }, "outputs": [ { @@ -1168,9 +1179,187 @@ " plt.title(f'Prediction: {pred}')\n", " plt.imshow(np.array(img), cmap='gray') " ] + }, + { + "cell_type": "markdown", + "id": "155eaf2f", + "metadata": { + "id": "155eaf2f" + }, + "source": [ + "## Perceiver AR" + ] + }, + { + "cell_type": "markdown", + "id": "509230ba", + "metadata": { + "id": "509230ba" + }, + "source": [ + "This section demonstrates how a small Perceiver AR model (30.7M parameters), [trained](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#language-model-pretraining-clm) on WikiText-103-raw, generates text from a given prompt. The model was trained with sequences of length of `4096`, tokenized with a UTF-8 bytes tokenizer. It generates text by predicting raw UTF-8 bytes. We first need a `TextPreprocessor` with the corresponding settings." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b04216d4", + "metadata": { + "id": "b04216d4" + }, + "outputs": [], + "source": [ + "from perceiver.data.text import TextPreprocessor\n", + "\n", + "# Text Proprocessor uses a UTF-8 bytes tokenizer\n", + "preproc = TextPreprocessor(tokenizer=\"deepmind/language-perceiver\", max_seq_len=4096, add_special_tokens=False)" + ] + }, + { + "cell_type": "markdown", + "id": "12c350f9", + "metadata": { + "id": "12c350f9" + }, + "source": [ + "We load the model from a training checkpoint," + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "555cc38c", + "metadata": { + "id": "555cc38c" + }, + "outputs": [], + "source": [ + "from perceiver.model.text.clm import LitCausalLanguageModel\n", + "\n", + "# Text generation quite slow on a CPU, use GPU is available\n", + "device = \"cuda\" if torch.cuda.is_available else \"cpu\"\n", + "\n", + "ckpt = \"logs/clm_pre/version_0/checkpoints/epoch=005-val_loss=0.955.ckpt\"\n", + "model = LitCausalLanguageModel.load_from_checkpoint(ckpt).model.eval().to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "1fac32ef", + "metadata": { + "id": "1fac32ef" + }, + "source": [ + "use a picked token sequence of length `4096` from the WikiText-103-raw validation set as `prompt` and generate `512` tokens. Tokens are generated with top-k sampling where k is a function of the vocabulary size and parameter `threshold`. The generated text is colored red." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "701d1cfe", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "701d1cfe", + "outputId": "fd0e99b6-467b-4ee4-862c-47e490d785ef" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "of Michigan at the Brule River, crossing into Florence County, Wisconsin for about 14 miles ( 23 km ). \n", + " = = = Eastern segment = = = \n", + " US 2 / US 141 re @-@ enters Michigan where it crosses the Menominee River and subsequently meets M ‑ 95 in Breitung Township north of Iron Mountain and Kingsford. The highways merge in a triple concurrency and run south on Stephenson Avenue into Iron Mountain along the west side of Lake Antoine, parallel to a branch line of the Escanaba and Lake Superior Railroad ( ELS Railroad ). The road crosses through a retail corridor and over a flooded pit of the Chapin Mine. In downtown Iron Mountain at Ludington Street, M ‑ 95 turns west off Stephenson Avenue to run across town to Kingsford. US 2 / US 141 exits downtown and turns east along a second retail corridor near the Midtown Mall. The highway re @-@ enters Breitung Township where US 141 separates to the south to re @-@ enter Wisconsin. US 2 continues eastward parallel to a branch of the Canadian National Railway ( CN Railway ). Both road and rail travel through the community of Quinnesec, where they pass near the largest paper mill in the UP. The trunkline runs along the main street of Norway, where the highway meets the eastern terminus of US 8. Then US 2 continues east through rural Dickinson County to Vulcan, passing north of Hanbury Lake through the Copper Country State Forest, before crossing the Sturgeon River in Loretto and passing into Menominee County. \n", + " In Menominee County, the environment takes on a more agricultural character along US 2. The highway passes through the edge of the community of Hermansville before entering Powers. US 2 comes to a three @-@ way intersection and turns northeast merging onto US 41. The concurrent highway runs from Powers through the communities of Wilson and Spaulding on the south side of the CN Railway. At Harris, the trunkline enters the Hannahville Indian Community. Harris is on the Menominee County side of the reservation, but as the highway continues east, it crosses over to Bark River on the Delta County side. The county line in between not only separates the two communities, but also serves as the boundary between the Central and Eastern time zones. East of Bark River, the highway crosses the community's namesake waterway before intersecting the eastern terminus of M ‑ 69. The roadway crosses the Ford River prior to turning due east into the outskirts of Escanaba. \n", + " US 2 / US 41 widens to four lanes along Ludington Street, which forms the east – west axis of the Escanaba street grid. Near downtown, the highway meets M ‑ 35, which runs along the city's north – south axis, Lincoln Avenue. The trunklines merge and run north, bypassing the traditional central business district for a different business corridor. Lincoln Avenue runs north carrying four lanes of traffic past the Upper Peninsula State Fairgrounds, site of one of the two state fairs for the state of Michigan, the only state to have twin fairs. US 2 / US 41 / M ‑ 35 continues north on Lincoln Avenue past the campus of Bay de Noc Community College. The four @-@ lane highway crosses the Escanaba River just upstream from its mouth near the large Mead Paper Mill and shifts to run immediately next to Little Bay de Noc. The section here carried the highest traffic counts along all of US 2 in the state : an average of 23 @,@ 977 vehicles used this segment of roadway daily in 2011. \n", + " The road turns inland again, and US 2 / US 41 / M ‑ 35 passes to the west of downtown Gladstone. The highway through here is an expressway, four lanes divided by a central median and no driveway access. Unlike a freeway, the expressway has standard intersections and not interchanges. The highway intersects the eastern terminus of County Road 426 ( CR 426 ) and crosses the ELS Railroad south of the stoplight for 4th Avenue North, where M ‑ 35 separates from the US Highways and turns to the northwest. The expressway continues north parallel to the CN Railway, crossing the Days River. Throug\u001b[31mh the crossing of the lanes, the crosses must protect and replace cars that weigh along intersect the westbound turns before accessing CR 451 and CR 45. The route continues commutically accommodate to the west, though the state is not fully enough until the neighborhood of Centerville its northern terminus in Walkien. \n", + " Upon returning to the northeast, County Highway is a local daily southern terminus in Seattle, defensive linking to the Adarda Province. \n", + " There are much larger best state linked thro\u001b[0m\n" + ] + } + ], + "source": [ + "from termcolor import colored\n", + "\n", + "# 4096 tokens from a text passage in the WikiText-103-raw validation set\n", + "prompt = [117, 108, 38, 83, 111, 105, 110, 111, 109, 103, 116, 38, 103, 122, 38, 122, 110, 107, 38, 72, 120, 123, 114, 107, 38, 88, 111, 124, 107, 120, 38, 50, 38, 105, 120, 117, 121, 121, 111, 116, 109, 38, 111, 116, 122, 117, 38, 76, 114, 117, 120, 107, 116, 105, 107, 38, 73, 117, 123, 116, 122, 127, 38, 50, 38, 93, 111, 121, 105, 117, 116, 121, 111, 116, 38, 108, 117, 120, 38, 103, 104, 117, 123, 122, 38, 55, 58, 38, 115, 111, 114, 107, 121, 38, 46, 38, 56, 57, 38, 113, 115, 38, 47, 38, 52, 38, 16, 38, 67, 38, 67, 38, 67, 38, 75, 103, 121, 122, 107, 120, 116, 38, 121, 107, 109, 115, 107, 116, 122, 38, 67, 38, 67, 38, 67, 38, 16, 38, 91, 89, 38, 56, 38, 53, 38, 91, 89, 38, 55, 58, 55, 38, 120, 107, 38, 70, 51, 70, 38, 107, 116, 122, 107, 120, 121, 38, 83, 111, 105, 110, 111, 109, 103, 116, 38, 125, 110, 107, 120, 107, 38, 111, 122, 38, 105, 120, 117, 121, 121, 107, 121, 38, 122, 110, 107, 38, 83, 107, 116, 117, 115, 111, 116, 107, 107, 38, 88, 111, 124, 107, 120, 38, 103, 116, 106, 38, 121, 123, 104, 121, 107, 119, 123, 107, 116, 122, 114, 127, 38, 115, 107, 107, 122, 121, 38, 83, 38, 232, 134, 151, 38, 63, 59, 38, 111, 116, 38, 72, 120, 107, 111, 122, 123, 116, 109, 38, 90, 117, 125, 116, 121, 110, 111, 118, 38, 116, 117, 120, 122, 110, 38, 117, 108, 38, 79, 120, 117, 116, 38, 83, 117, 123, 116, 122, 103, 111, 116, 38, 103, 116, 106, 38, 81, 111, 116, 109, 121, 108, 117, 120, 106, 38, 52, 38, 90, 110, 107, 38, 110, 111, 109, 110, 125, 103, 127, 121, 38, 115, 107, 120, 109, 107, 38, 111, 116, 38, 103, 38, 122, 120, 111, 118, 114, 107, 38, 105, 117, 116, 105, 123, 120, 120, 107, 116, 105, 127, 38, 103, 116, 106, 38, 120, 123, 116, 38, 121, 117, 123, 122, 110, 38, 117, 116, 38, 89, 122, 107, 118, 110, 107, 116, 121, 117, 116, 38, 71, 124, 107, 116, 123, 107, 38, 111, 116, 122, 117, 38, 79, 120, 117, 116, 38, 83, 117, 123, 116, 122, 103, 111, 116, 38, 103, 114, 117, 116, 109, 38, 122, 110, 107, 38, 125, 107, 121, 122, 38, 121, 111, 106, 107, 38, 117, 108, 38, 82, 103, 113, 107, 38, 71, 116, 122, 117, 111, 116, 107, 38, 50, 38, 118, 103, 120, 103, 114, 114, 107, 114, 38, 122, 117, 38, 103, 38, 104, 120, 103, 116, 105, 110, 38, 114, 111, 116, 107, 38, 117, 108, 38, 122, 110, 107, 38, 75, 121, 105, 103, 116, 103, 104, 103, 38, 103, 116, 106, 38, 82, 103, 113, 107, 38, 89, 123, 118, 107, 120, 111, 117, 120, 38, 88, 103, 111, 114, 120, 117, 103, 106, 38, 46, 38, 75, 82, 89, 38, 88, 103, 111, 114, 120, 117, 103, 106, 38, 47, 38, 52, 38, 90, 110, 107, 38, 120, 117, 103, 106, 38, 105, 120, 117, 121, 121, 107, 121, 38, 122, 110, 120, 117, 123, 109, 110, 38, 103, 38, 120, 107, 122, 103, 111, 114, 38, 105, 117, 120, 120, 111, 106, 117, 120, 38, 103, 116, 106, 38, 117, 124, 107, 120, 38, 103, 38, 108, 114, 117, 117, 106, 107, 106, 38, 118, 111, 122, 38, 117, 108, 38, 122, 110, 107, 38, 73, 110, 103, 118, 111, 116, 38, 83, 111, 116, 107, 38, 52, 38, 79, 116, 38, 106, 117, 125, 116, 122, 117, 125, 116, 38, 79, 120, 117, 116, 38, 83, 117, 123, 116, 122, 103, 111, 116, 38, 103, 122, 38, 82, 123, 106, 111, 116, 109, 122, 117, 116, 38, 89, 122, 120, 107, 107, 122, 38, 50, 38, 83, 38, 232, 134, 151, 38, 63, 59, 38, 122, 123, 120, 116, 121, 38, 125, 107, 121, 122, 38, 117, 108, 108, 38, 89, 122, 107, 118, 110, 107, 116, 121, 117, 116, 38, 71, 124, 107, 116, 123, 107, 38, 122, 117, 38, 120, 123, 116, 38, 103, 105, 120, 117, 121, 121, 38, 122, 117, 125, 116, 38, 122, 117, 38, 81, 111, 116, 109, 121, 108, 117, 120, 106, 38, 52, 38, 91, 89, 38, 56, 38, 53, 38, 91, 89, 38, 55, 58, 55, 38, 107, 126, 111, 122, 121, 38, 106, 117, 125, 116, 122, 117, 125, 116, 38, 103, 116, 106, 38, 122, 123, 120, 116, 121, 38, 107, 103, 121, 122, 38, 103, 114, 117, 116, 109, 38, 103, 38, 121, 107, 105, 117, 116, 106, 38, 120, 107, 122, 103, 111, 114, 38, 105, 117, 120, 120, 111, 106, 117, 120, 38, 116, 107, 103, 120, 38, 122, 110, 107, 38, 83, 111, 106, 122, 117, 125, 116, 38, 83, 103, 114, 114, 38, 52, 38, 90, 110, 107, 38, 110, 111, 109, 110, 125, 103, 127, 38, 120, 107, 38, 70, 51, 70, 38, 107, 116, 122, 107, 120, 121, 38, 72, 120, 107, 111, 122, 123, 116, 109, 38, 90, 117, 125, 116, 121, 110, 111, 118, 38, 125, 110, 107, 120, 107, 38, 91, 89, 38, 55, 58, 55, 38, 121, 107, 118, 103, 120, 103, 122, 107, 121, 38, 122, 117, 38, 122, 110, 107, 38, 121, 117, 123, 122, 110, 38, 122, 117, 38, 120, 107, 38, 70, 51, 70, 38, 107, 116, 122, 107, 120, 38, 93, 111, 121, 105, 117, 116, 121, 111, 116, 38, 52, 38, 91, 89, 38, 56, 38, 105, 117, 116, 122, 111, 116, 123, 107, 121, 38, 107, 103, 121, 122, 125, 103, 120, 106, 38, 118, 103, 120, 103, 114, 114, 107, 114, 38, 122, 117, 38, 103, 38, 104, 120, 103, 116, 105, 110, 38, 117, 108, 38, 122, 110, 107, 38, 73, 103, 116, 103, 106, 111, 103, 116, 38, 84, 103, 122, 111, 117, 116, 103, 114, 38, 88, 103, 111, 114, 125, 103, 127, 38, 46, 38, 73, 84, 38, 88, 103, 111, 114, 125, 103, 127, 38, 47, 38, 52, 38, 72, 117, 122, 110, 38, 120, 117, 103, 106, 38, 103, 116, 106, 38, 120, 103, 111, 114, 38, 122, 120, 103, 124, 107, 114, 38, 122, 110, 120, 117, 123, 109, 110, 38, 122, 110, 107, 38, 105, 117, 115, 115, 123, 116, 111, 122, 127, 38, 117, 108, 38, 87, 123, 111, 116, 116, 107, 121, 107, 105, 38, 50, 38, 125, 110, 107, 120, 107, 38, 122, 110, 107, 127, 38, 118, 103, 121, 121, 38, 116, 107, 103, 120, 38, 122, 110, 107, 38, 114, 103, 120, 109, 107, 121, 122, 38, 118, 103, 118, 107, 120, 38, 115, 111, 114, 114, 38, 111, 116, 38, 122, 110, 107, 38, 91, 86, 38, 52, 38, 90, 110, 107, 38, 122, 120, 123, 116, 113, 114, 111, 116, 107, 38, 120, 123, 116, 121, 38, 103, 114, 117, 116, 109, 38, 122, 110, 107, 38, 115, 103, 111, 116, 38, 121, 122, 120, 107, 107, 122, 38, 117, 108, 38, 84, 117, 120, 125, 103, 127, 38, 50, 38, 125, 110, 107, 120, 107, 38, 122, 110, 107, 38, 110, 111, 109, 110, 125, 103, 127, 38, 115, 107, 107, 122, 121, 38, 122, 110, 107, 38, 107, 103, 121, 122, 107, 120, 116, 38, 122, 107, 120, 115, 111, 116, 123, 121, 38, 117, 108, 38, 91, 89, 38, 62, 38, 52, 38, 90, 110, 107, 116, 38, 91, 89, 38, 56, 38, 105, 117, 116, 122, 111, 116, 123, 107, 121, 38, 107, 103, 121, 122, 38, 122, 110, 120, 117, 123, 109, 110, 38, 120, 123, 120, 103, 114, 38, 74, 111, 105, 113, 111, 116, 121, 117, 116, 38, 73, 117, 123, 116, 122, 127, 38, 122, 117, 38, 92, 123, 114, 105, 103, 116, 38, 50, 38, 118, 103, 121, 121, 111, 116, 109, 38, 116, 117, 120, 122, 110, 38, 117, 108, 38, 78, 103, 116, 104, 123, 120, 127, 38, 82, 103, 113, 107, 38, 122, 110, 120, 117, 123, 109, 110, 38, 122, 110, 107, 38, 73, 117, 118, 118, 107, 120, 38, 73, 117, 123, 116, 122, 120, 127, 38, 89, 122, 103, 122, 107, 38, 76, 117, 120, 107, 121, 122, 38, 50, 38, 104, 107, 108, 117, 120, 107, 38, 105, 120, 117, 121, 121, 111, 116, 109, 38, 122, 110, 107, 38, 89, 122, 123, 120, 109, 107, 117, 116, 38, 88, 111, 124, 107, 120, 38, 111, 116, 38, 82, 117, 120, 107, 122, 122, 117, 38, 103, 116, 106, 38, 118, 103, 121, 121, 111, 116, 109, 38, 111, 116, 122, 117, 38, 83, 107, 116, 117, 115, 111, 116, 107, 107, 38, 73, 117, 123, 116, 122, 127, 38, 52, 38, 16, 38, 79, 116, 38, 83, 107, 116, 117, 115, 111, 116, 107, 107, 38, 73, 117, 123, 116, 122, 127, 38, 50, 38, 122, 110, 107, 38, 107, 116, 124, 111, 120, 117, 116, 115, 107, 116, 122, 38, 122, 103, 113, 107, 121, 38, 117, 116, 38, 103, 38, 115, 117, 120, 107, 38, 103, 109, 120, 111, 105, 123, 114, 122, 123, 120, 103, 114, 38, 105, 110, 103, 120, 103, 105, 122, 107, 120, 38, 103, 114, 117, 116, 109, 38, 91, 89, 38, 56, 38, 52, 38, 90, 110, 107, 38, 110, 111, 109, 110, 125, 103, 127, 38, 118, 103, 121, 121, 107, 121, 38, 122, 110, 120, 117, 123, 109, 110, 38, 122, 110, 107, 38, 107, 106, 109, 107, 38, 117, 108, 38, 122, 110, 107, 38, 105, 117, 115, 115, 123, 116, 111, 122, 127, 38, 117, 108, 38, 78, 107, 120, 115, 103, 116, 121, 124, 111, 114, 114, 107, 38, 104, 107, 108, 117, 120, 107, 38, 107, 116, 122, 107, 120, 111, 116, 109, 38, 86, 117, 125, 107, 120, 121, 38, 52, 38, 91, 89, 38, 56, 38, 105, 117, 115, 107, 121, 38, 122, 117, 38, 103, 38, 122, 110, 120, 107, 107, 38, 70, 51, 70, 38, 125, 103, 127, 38, 111, 116, 122, 107, 120, 121, 107, 105, 122, 111, 117, 116, 38, 103, 116, 106, 38, 122, 123, 120, 116, 121, 38, 116, 117, 120, 122, 110, 107, 103, 121, 122, 38, 115, 107, 120, 109, 111, 116, 109, 38, 117, 116, 122, 117, 38, 91, 89, 38, 58, 55, 38, 52, 38, 90, 110, 107, 38, 105, 117, 116, 105, 123, 120, 120, 107, 116, 122, 38, 110, 111, 109, 110, 125, 103, 127, 38, 120, 123, 116, 121, 38, 108, 120, 117, 115, 38, 86, 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122, 38, 108, 117, 120, 38, 58, 122, 110, 38, 71, 124, 107, 116, 123, 107, 38, 84, 117, 120, 122, 110, 38, 50, 38, 125, 110, 107, 120, 107, 38, 83, 38, 232, 134, 151, 38, 57, 59, 38, 121, 107, 118, 103, 120, 103, 122, 107, 121, 38, 108, 120, 117, 115, 38, 122, 110, 107, 38, 91, 89, 38, 78, 111, 109, 110, 125, 103, 127, 121, 38, 103, 116, 106, 38, 122, 123, 120, 116, 121, 38, 122, 117, 38, 122, 110, 107, 38, 116, 117, 120, 122, 110, 125, 107, 121, 122, 38, 52, 38, 90, 110, 107, 38, 107, 126, 118, 120, 107, 121, 121, 125, 103, 127, 38, 105, 117, 116, 122, 111, 116, 123, 107, 121, 38, 116, 117, 120, 122, 110, 38, 118, 103, 120, 103, 114, 114, 107, 114, 38, 122, 117, 38, 122, 110, 107, 38, 73, 84, 38, 88, 103, 111, 114, 125, 103, 127, 38, 50, 38, 105, 120, 117, 121, 121, 111, 116, 109, 38, 122, 110, 107, 38, 74, 103, 127, 121, 38, 88, 111, 124, 107, 120, 38, 52, 38, 90, 110, 120, 117, 123, 109]\n", + "prompt = torch.tensor(prompt).to(device)\n", + "\n", + "generated = model.generate(num=512, prompt=prompt[None, ...], threshold=0.9, temperature=1.0)[0]\n", + "print(f\"{preproc.tokenizer.decode(prompt)}{colored(preproc.tokenizer.decode(generated), 'red')}\")" + ] + }, + { + "cell_type": "markdown", + "id": "4ca2ff77", + "metadata": { + "id": "4ca2ff77" + }, + "source": [ + "For better generalization to shorter sequences, we use another checkpoint that was additionally trained with command line option `--model.random_sequence_trucation=true` (details [here](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#language-model-pretraining-clm))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "eaa24ac0", + "metadata": { + "id": "eaa24ac0" + }, + "outputs": [], + "source": [ + "ckpt = \"logs/clm_pre/version_1/checkpoints/epoch=005-val_loss=0.973.ckpt\"\n", + "model = LitCausalLanguageModel.load_from_checkpoint(ckpt).model.eval().to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "1663aaeb", + "metadata": { + "id": "1663aaeb" + }, + "source": [ + "and generate text for a much shorter prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "96fdc9b7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "96fdc9b7", + "outputId": "932f9dcd-8e54-493f-d058-84df044905b3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A man was reading a book on a sunny day until he sudden\u001b[31mly engaged a brigade against the Ashburne crimes. \n", + " Along with the book highlighted the Secretariat of the Anti @-@ Bahnai Famack struggles with the Sensibility of Christian Kuiser, Siegan had called upon the concept of the Mummifka Corps in 1214. The dead @-@ minden wooden forces were then known dubiously but after heading claims had peace. \n", + " Elephantic haematist John Carnoff wrote that the difficulty intended for them deeply begun. Recent differences took over by Siegan in the difference and attempte\u001b[0m\n" + ] + } + ], + "source": [ + "prompt_text = \"A man was reading a book on a sunny day until he sudden\"\n", + "prompt, _ = preproc.preprocess(prompt_text)\n", + "prompt = prompt.to(device)\n", + "\n", + "generated = model.generate(num=512, prompt=prompt[None, ...], threshold=0.9, temperature=1.0)[0]\n", + "print(f\"{preproc.tokenizer.decode(prompt)}{colored(preproc.tokenizer.decode(generated), 'red')}\")" + ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", @@ -1188,12 +1377,9 @@ "pygments_lexer": "ipython3", "version": "3.7.12" }, - "colab": { - "provenance": [] - }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "81891bda64b54c58b04a432e8279728f": { + "d25a1c2ce70846c1aae777a24fcbea20": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1208,14 +1394,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - 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"style": "IPY_MODEL_ace91553c64245be8c4c2087b4efce03", - "value": " 4542/4542 [00:00<00:00, 91231.61it/s]" + "style": "IPY_MODEL_65f2842955c5459a95b7e56670ef0cd0", + "value": " 4542/4542 [00:00<00:00, 155584.37it/s]" } }, - "a8ea3198294d4c688844615ef4087fc6": { + "e3fc10667e1f4955914876213ecb8fe2": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -4591,7 +4777,7 @@ "width": null } }, - "01e693ede66a46f3974153dae1575468": { + "f2b190becab3434083251ab66c56ceda": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -4606,15 +4792,16 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_25821d1826c8413c877c803e8731cf61", - "IPY_MODEL_a215e5a143b44dc58af891af82e31fd2", - "IPY_MODEL_21927eeb34514526954e8ee09f2f5ef0" + "IPY_MODEL_676aa7e9649e43c9b9863dffdacd6882", + "IPY_MODEL_2ccd6cc868be46a0884ec9c81ba3cc3f", + "IPY_MODEL_a2ca9205fe0c4f02b79a6de7cc101757" ], - "layout": "IPY_MODEL_a8ea3198294d4c688844615ef4087fc6" + "layout": "IPY_MODEL_e3fc10667e1f4955914876213ecb8fe2" } } } - } + }, + "accelerator": "GPU" }, "nbformat": 4, "nbformat_minor": 5 diff --git a/perceiver/data/text/__init__.py b/perceiver/data/text/__init__.py index 9bc284d..e233bdf 100644 --- a/perceiver/data/text/__init__.py +++ b/perceiver/data/text/__init__.py @@ -1,5 +1,6 @@ from perceiver.data.text.bookcorpus import BookCorpusDataModule from perceiver.data.text.common import TextPreprocessor +from perceiver.data.text.enwik8 import Enwik8DataModule from perceiver.data.text.imdb import ImdbDataModule from perceiver.data.text.wikibook import WikiBookDataModule from perceiver.data.text.wikipedia import WikipediaDataModule diff --git a/perceiver/data/text/collator.py b/perceiver/data/text/collator.py index cdd18b0..0c637a7 100644 --- a/perceiver/data/text/collator.py +++ b/perceiver/data/text/collator.py @@ -23,6 +23,8 @@ def __call__(self, examples): class DefaultCollator(Collator): + label_keys = ["label", "label_ids"] + def __init__(self, tokenizer: PreTrainedTokenizerFast, max_seq_len: Optional[int] = None): self.collator = DefaultDataCollator() self.tokenizer = tokenizer @@ -49,7 +51,9 @@ def _prepare(self, example, max_length): truncation=True, ) - prepared["label"] = example["label"] + for label_key in self.label_keys: + if label_key in example: + prepared[label_key] = example[label_key] return prepared diff --git a/perceiver/data/text/common.py b/perceiver/data/text/common.py index 0ac4856..40afb88 100644 --- a/perceiver/data/text/common.py +++ b/perceiver/data/text/common.py @@ -1,11 +1,11 @@ import hashlib import os from itertools import chain -from typing import Any, Sequence +from typing import Any, Optional, Sequence import pytorch_lightning as pl import torch -from datasets import DatasetDict +from datasets import Dataset, DatasetDict from torch.utils.data import DataLoader from transformers import AutoTokenizer @@ -164,8 +164,10 @@ def chunk_dataset( batch_size: int, include_keys: Sequence[str] = ("input_ids", "word_ids"), remove_keys: Sequence[str] = (), + max_seq_len: Optional[int] = None, ): - max_seq_len = self.hparams.max_seq_len + if max_seq_len is None: + max_seq_len = self.hparams.max_seq_len def chunk(*args): chained = {k: list(chain(*args[i])) for i, k in enumerate(include_keys)} @@ -187,3 +189,26 @@ def chunk(*args): desc=f"Split dataset into chunks of size {max_seq_len}", ) return result + + +class ClmDatasetWrapper(torch.utils.data.Dataset): + def __init__(self, dataset: Dataset, max_seq_len: int, random_shift: bool = False): + self.dataset = dataset + self.max_seq_len = max_seq_len + self.random_shift = random_shift + + def __getitem__(self, idx): + if self.random_shift: + shift = torch.randint(self.max_seq_len + 1, (1,)).item() + record_1 = self.dataset[idx]["input_ids"] + record_2 = self.dataset[idx + 1]["input_ids"] + record = record_1[shift:] + record_2[:shift] + else: + record = self.dataset[idx]["input_ids"] + return {"input_ids": record[:-1], "label_ids": record[1:]} + + def __len__(self): + if self.random_shift: + return len(self.dataset) - 1 + else: + return len(self.dataset) diff --git a/perceiver/data/text/enwik8.py b/perceiver/data/text/enwik8.py new file mode 100644 index 0000000..a2ce24d --- /dev/null +++ b/perceiver/data/text/enwik8.py @@ -0,0 +1,52 @@ +import os +from typing import Any, Union + +from datasets import Dataset, DatasetDict, load_dataset + +from perceiver.data.text.collator import DefaultCollator +from perceiver.data.text.common import ClmDatasetWrapper, TextDataModule + + +class Enwik8DataModule(TextDataModule): + def __init__( + self, + *args: Any, + dataset_dir: str = os.path.join(".cache", "enwik8"), + **kwargs: Any, + ): + super().__init__(*args, **kwargs) + self.collator = DefaultCollator(tokenizer=self.tokenizer, max_seq_len=self.hparams.max_seq_len) + + def prepare_data(self) -> None: + if not os.path.exists(self.preproc_dir): + dataset = load_dataset("enwik8", "enwik8", split="train", cache_dir=self.hparams.dataset_dir) + self._preproc_dataset(dataset) + + def setup(self, stage=None): + super().setup(stage) + self.ds_train = ClmDatasetWrapper(self.ds_train, max_seq_len=self.hparams.max_seq_len, random_shift=True) + self.ds_valid = ClmDatasetWrapper(self.ds_valid, max_seq_len=self.hparams.max_seq_len, random_shift=False) + + def _load_dataset(self): + return DatasetDict.load_from_disk(os.path.join(self.preproc_dir, "chunked")) + + def _preproc_dataset( + self, + dataset: Dataset, + batch_size: int = 1000, + train_size: Union[float, int, None] = None, + valid_size: Union[float, int, None] = 0.05, + ): + def append_newline(example): + return {"text": example["text"] + "\n"} + + dataset = dataset.map(append_newline, num_proc=max(self.hparams.num_workers, 1)) + dataset = dataset.train_test_split(train_size=train_size, test_size=valid_size, shuffle=False) + dataset = self.tokenize_dataset(dataset, batch_size=batch_size, return_word_ids=False) + dataset = self.chunk_dataset( + DatasetDict(train=dataset["train"], valid=dataset["test"]), + include_keys=["input_ids"], + batch_size=batch_size, + max_seq_len=self.hparams.max_seq_len + 1, + ) + dataset.save_to_disk(os.path.join(self.preproc_dir, "chunked")) diff --git a/perceiver/data/text/imdb.py b/perceiver/data/text/imdb.py index 423c524..b5cc4a8 100644 --- a/perceiver/data/text/imdb.py +++ b/perceiver/data/text/imdb.py @@ -9,8 +9,8 @@ class Task(Enum): - mlm = 0 - clf = 1 + mlm = 0 # masked language modeling + clf = 1 # sequence classification class ImdbDataModule(TextDataModule): @@ -18,18 +18,18 @@ def __init__( self, *args: Any, dataset_dir: str = os.path.join(".cache", "imdb"), - target_task: Task = Task.mlm, + task: Task = Task.mlm, mask_prob: float = 0.15, **kwargs: Any, ): super().__init__(*args, **kwargs) - if target_task == Task.mlm: + if task == Task.mlm: self.collator = WordMaskingCollator(tokenizer=self.tokenizer, mask_prob=mask_prob) - elif target_task == Task.clf: + elif task == Task.clf: self.collator = DefaultCollator(tokenizer=self.tokenizer, max_seq_len=self.hparams.max_seq_len) else: - raise ValueError(f"Invalid target task {target_task}") + raise ValueError(f"Invalid task {task}") @property def num_classes(self): @@ -41,7 +41,7 @@ def prepare_data(self) -> None: self._preproc_dataset(dataset) def _load_dataset(self): - subdir = "tokenized" if self.hparams.target_task == Task.clf else "chunked" + subdir = "tokenized" if self.hparams.task == Task.clf else "chunked" return DatasetDict.load_from_disk(os.path.join(self.preproc_dir, subdir)) def _preproc_dataset(self, dataset: DatasetDict, batch_size: int = 1000): diff --git a/perceiver/data/text/wikipedia.py b/perceiver/data/text/wikipedia.py index 13b136b..cc0d001 100644 --- a/perceiver/data/text/wikipedia.py +++ b/perceiver/data/text/wikipedia.py @@ -1,7 +1,7 @@ import os from typing import Any, Union -from datasets import DatasetDict, load_dataset +from datasets import Dataset, DatasetDict, load_dataset from perceiver.data.text.collator import WordMaskingCollator from perceiver.data.text.common import TextDataModule @@ -28,7 +28,7 @@ def _load_dataset(self): def _preproc_dataset( self, - dataset: DatasetDict, + dataset: Dataset, batch_size: int = 1000, train_size: Union[float, int, None] = None, valid_size: Union[float, int, None] = 0.05, diff --git a/perceiver/data/text/wikitext.py b/perceiver/data/text/wikitext.py index f2ff2c9..193488f 100644 --- a/perceiver/data/text/wikitext.py +++ b/perceiver/data/text/wikitext.py @@ -1,11 +1,17 @@ import os import re +from enum import Enum from typing import Any, Optional from datasets import DatasetDict, load_dataset -from perceiver.data.text.collator import WordMaskingCollator -from perceiver.data.text.common import TextDataModule +from perceiver.data.text.collator import DefaultCollator, WordMaskingCollator +from perceiver.data.text.common import ClmDatasetWrapper, TextDataModule + + +class Task(Enum): + mlm = 0 # masked language modeling + clm = 1 # causal language modeling class WikiTextDataModule(TextDataModule): @@ -14,13 +20,19 @@ def __init__( *args: Any, dataset_dir: str = os.path.join(".cache", "wikitext"), config_name: Optional[str] = None, + task: Task = Task.mlm, mask_prob: float = 0.15, filter_empty: bool = False, filter_headers: bool = False, **kwargs: Any, ): super().__init__(*args, **kwargs) - self.collator = WordMaskingCollator(tokenizer=self.tokenizer, mask_prob=mask_prob) + if task == Task.mlm: + self.collator = WordMaskingCollator(tokenizer=self.tokenizer, mask_prob=mask_prob) + elif task == Task.clm: + self.collator = DefaultCollator(tokenizer=self.tokenizer, max_seq_len=self.hparams.max_seq_len) + else: + raise ValueError(f"Invalid task {task}") def prepare_data(self) -> None: if not os.path.exists(self.preproc_dir): @@ -28,16 +40,27 @@ def prepare_data(self) -> None: dataset = load_dataset("wikitext", config_name, cache_dir=self.hparams.dataset_dir) self._preproc_dataset(dataset) + def setup(self, stage=None): + super().setup(stage) + if self.hparams.task == Task.clm: + self.ds_train = ClmDatasetWrapper(self.ds_train, max_seq_len=self.hparams.max_seq_len, random_shift=True) + self.ds_valid = ClmDatasetWrapper(self.ds_valid, max_seq_len=self.hparams.max_seq_len, random_shift=False) + def _load_dataset(self): return DatasetDict.load_from_disk(os.path.join(self.preproc_dir, "chunked")) def _preproc_dataset(self, dataset: DatasetDict, batch_size: int = 1000): dataset = self._filter_dataset(dataset) dataset = self.tokenize_dataset(dataset, batch_size=batch_size) - dataset = self.chunk_dataset( - DatasetDict(train=dataset["train"], valid=dataset["validation"]), - batch_size=batch_size, - ) + dataset = DatasetDict(train=dataset["train"], valid=dataset["validation"]) + + if self.hparams.task == Task.mlm: + dataset = self.chunk_dataset(dataset, batch_size=batch_size) + elif self.hparams.task == Task.clm: + dataset = self.chunk_dataset( + dataset, batch_size=batch_size, include_keys=["input_ids"], max_seq_len=self.hparams.max_seq_len + 1 + ) + dataset.save_to_disk(os.path.join(self.preproc_dir, "chunked")) def _filter_dataset(self, dataset: DatasetDict): @@ -76,4 +99,6 @@ def _preproc_dir_hash_input(self) -> str: hash_input = f"{hash_input}-fe" if self.hparams.filter_headers: hash_input = f"{hash_input}-fh" + if self.hparams.task == Task.clm: + hash_input = f"{hash_input}-clm" return hash_input diff --git a/perceiver/model/core/classifier.py b/perceiver/model/core/classifier.py new file mode 100644 index 0000000..edc3ac8 --- /dev/null +++ b/perceiver/model/core/classifier.py @@ -0,0 +1,26 @@ +from typing import Optional + +import torch +import torch.nn as nn + +from perceiver.model.core import OutputAdapter +from perceiver.model.core.config import ClassificationDecoderConfig # noqa: F401 + + +class ClassificationOutputAdapter(OutputAdapter): + def __init__( + self, + num_classes: int, + num_output_queries: int = 1, + num_output_query_channels: Optional[int] = None, + init_scale: float = 0.02, + ): + + if num_output_query_channels is None: + num_output_query_channels = num_classes + + super().__init__(output_query=torch.empty(num_output_queries, num_output_query_channels), init_scale=init_scale) + self.linear = nn.Linear(num_output_query_channels, num_classes) + + def forward(self, x): + return self.linear(x).squeeze(dim=1) diff --git a/perceiver/model/core/config.py b/perceiver/model/core/config.py index 0724df9..4fcb7a6 100644 --- a/perceiver/model/core/config.py +++ b/perceiver/model/core/config.py @@ -22,7 +22,7 @@ class EncoderConfig: freeze: bool = False def base_kwargs(self, exclude=("freeze",)): - return _base_kwargs(self, EncoderConfig, exclude) + return base_kwargs(self, EncoderConfig, exclude) @dataclass @@ -37,7 +37,7 @@ class DecoderConfig: freeze: bool = False def base_kwargs(self, exclude=("freeze",)): - return _base_kwargs(self, DecoderConfig, exclude) + return base_kwargs(self, DecoderConfig, exclude) E = TypeVar("E", bound=EncoderConfig) @@ -55,13 +55,17 @@ class PerceiverConfig(Generic[E, D]): params: Optional[str] = None +def base_kwargs(config, base_class, exclude): + base_field_names = [field.name for field in fields(base_class) if field.name not in exclude] + return {k: v for k, v in asdict(config).items() if k in base_field_names} + + +# TODO: move to perceiver.model.core.classifier +# (still kept here for backward compatibility) + + @dataclass class ClassificationDecoderConfig(DecoderConfig): num_output_queries: int = 1 num_output_query_channels: int = 256 num_classes: int = 100 - - -def _base_kwargs(config, base_class, exclude): - base_field_names = [field.name for field in fields(base_class) if field.name not in exclude] - return {k: v for k, v in asdict(config).items() if k in base_field_names} diff --git a/perceiver/model/core/modules.py b/perceiver/model/core/modules.py index f6d065f..a0642a9 100644 --- a/perceiver/model/core/modules.py +++ b/perceiver/model/core/modules.py @@ -6,7 +6,8 @@ from fairscale.nn import checkpoint_wrapper from torch import Tensor -from perceiver.model.core.utils import Sequential +from perceiver.model.core.position import FrequencyPositionEncoding, RotaryPositionEmbedding +from perceiver.model.core.utils import init_parameters, Residual, Sequential class MultiHeadAttention(nn.Module): @@ -18,20 +19,24 @@ def __init__( num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, num_output_channels: Optional[int] = None, + causal_attention: bool = False, dropout: float = 0.0, + qkv_bias: bool = True, + out_bias: bool = True, ): - """Multi-head attention as described in https://arxiv.org/abs/2107.14795 Appendix E. + """Multi-head attention as specified in https://arxiv.org/abs/2107.14795 Appendix E plus support for rotary + position embeddings (https://arxiv.org/abs/2104.09864) and causal attention. :param num_heads: Number of attention heads. :param num_q_input_channels: Number of query input channels. :param num_kv_input_channels: Number of key/value input channels. - :param num_qk_channels: Number of channels query and key input channels are projected to, - for computing the attention matrix. Defaults to number `num_q_input_channels` - :param num_v_channels: Number of channels value input channels are projected to. - Defaults to `num_qk_channels`. - :param num_output_channels: Number of output channels attention result channels are projected to. - Defaults to `num_q_input_channels` - :param dropout: Dropout probability for attention matrix values. Defaults to `0.0` + :param num_qk_channels: Number of query and key channels. Default is number `num_q_input_channels` + :param num_v_channels: Number of value channels. Default is `num_qk_channels`. + :param num_output_channels: Number of output channels. Default is `num_q_input_channels` + :param causal_attention: Whether to apply a causal attention mask. Default is `False`. + :param dropout: Dropout probability for attention matrix values. Default is `0.0` + :param qkv_bias: Whether to use a bias term for query, key and value projections. Default is `True`. + :param qkv_bias: Whether to use a bias term for output projection. Default is `True`. """ super().__init__() @@ -54,44 +59,66 @@ def __init__( self.dp_scale = num_qk_channels_per_head ** -0.5 self.num_heads = num_heads + self.causal_attention = causal_attention - self.q_proj = nn.Linear(num_q_input_channels, num_qk_channels) - self.k_proj = nn.Linear(num_kv_input_channels, num_qk_channels) - self.v_proj = nn.Linear(num_kv_input_channels, num_v_channels) - self.o_proj = nn.Linear(num_v_channels, num_output_channels) + self.q_proj = nn.Linear(num_q_input_channels, num_qk_channels, bias=qkv_bias) + self.k_proj = nn.Linear(num_kv_input_channels, num_qk_channels, bias=qkv_bias) + self.v_proj = nn.Linear(num_kv_input_channels, num_v_channels, bias=qkv_bias) + self.o_proj = nn.Linear(num_v_channels, num_output_channels, bias=out_bias) self.dropout = nn.Dropout(dropout) - def forward(self, x_q, x_kv, pad_mask=None, attn_mask=None): + def forward( + self, + x_q, + x_kv, + pad_mask=None, + rot_pos_emb_q: Optional[RotaryPositionEmbedding] = None, + rot_pos_emb_k: Optional[RotaryPositionEmbedding] = None, + ): """ :param x_q: Query input of shape (B, N, D) where B is the batch size, N the query sequence length and D the number of query input channels (= `num_q_input_channels`) :param x_kv: Key/value input of shape (B, L, C) where B is the batch size, L the key/value sequence length and C are the number of key/value input channels (= `num_kv_input_channels`) :param pad_mask: Boolean key padding mask. `True` values indicate padding tokens. - :param attn_mask: Boolean attention mask. Not needed/supported yet. + :param rot_pos_emb_q: Applies a rotary position embedding to query i.e. if defined, rotates the query. + :param rot_pos_emb_k: Applies a rotary position embedding to key i.e. if defined, rotates the key. :return: attention result of shape (B, N, F) where B is the batch size, N the query sequence length and F the number of output channels (= `num_output_channels`) """ - if attn_mask is not None: - raise NotImplementedError("attention masks not supported yet") q = self.q_proj(x_q) k = self.k_proj(x_kv) v = self.v_proj(x_kv) - q, k, v = (rearrange(x, "b n (h c) -> (b h) n c", h=self.num_heads) for x in [q, k, v]) - attn = torch.einsum("b i c, b j c -> b i j", q, k) * self.dp_scale + q, k, v = (rearrange(x, "b n (h c) -> b h n c", h=self.num_heads) for x in [q, k, v]) + q = q * self.dp_scale + + if rot_pos_emb_q is not None: + q = rot_pos_emb_q.rotate(q) + + if rot_pos_emb_k is not None: + k = rot_pos_emb_k.rotate(k) + + attn = torch.einsum("b h i c, b h j c -> b h i j", q, k) + attn_max_neg = -torch.finfo(attn.dtype).max if pad_mask is not None: - pad_mask = repeat(pad_mask, "b j -> (b h) () j", h=self.num_heads) - attn_max_neg = -torch.finfo(attn.dtype).max + pad_mask = rearrange(pad_mask, "b j -> b 1 1 j") attn.masked_fill_(pad_mask, attn_max_neg) + if self.causal_attention: + i = q.shape[2] + j = k.shape[2] + + causal_mask = torch.ones((i, j), device=x_q.device, dtype=torch.bool).triu(j - i + 1) + attn.masked_fill_(causal_mask, attn_max_neg) + attn = attn.softmax(dim=-1) attn = self.dropout(attn) - o = torch.einsum("b i j, b j c -> b i c", attn, v) - o = rearrange(o, "(b h) n c -> b n (h c)", h=self.num_heads) + o = torch.einsum("b h i j, b h j c -> b h i c", attn, v) + o = rearrange(o, "b h n c -> b n (h c)", h=self.num_heads) return self.o_proj(o) @@ -104,9 +131,12 @@ def __init__( num_kv_input_channels: int, num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, + causal_attention: bool = False, dropout: float = 0.0, + qkv_bias: bool = True, + out_bias: bool = True, ): - """Multi-head cross-attention (see `MultiHeadAttention` for details).""" + """Pre-layer norm cross-attention (see `MultiHeadAttention` for attention details).""" super().__init__() self.q_norm = nn.LayerNorm(num_q_input_channels) self.kv_norm = nn.LayerNorm(num_kv_input_channels) @@ -116,15 +146,29 @@ def __init__( num_kv_input_channels=num_kv_input_channels, num_qk_channels=num_qk_channels, num_v_channels=num_v_channels, + causal_attention=causal_attention, dropout=dropout, + qkv_bias=qkv_bias, + out_bias=out_bias, ) - def forward(self, x_q, x_kv, pad_mask=None, attn_mask=None): - """Multi-head attention of query input `x_q` to key/value input (`x_kv`) after (separately) applying layer - normalization to these inputs.""" + def forward(self, x_q, x_kv=None, x_kv_prefix=None, pad_mask=None, rot_pos_emb_q=None, rot_pos_emb_k=None): + """Pre-layer norm cross-attention of query input `x_q` to key/value input (`x_kv` or `x_kv_prefix`). + + If `x_kv_prefix` is defined, the entire key/value input is assumed to be a concatenation of `x_kv_prefix` and + `x_q` along the sequence dimension. In this case, the query attends to itself at the end of the key/value + sequence (use case Perceiver AR). If `x_kv_prefix` is not defined, `x_kv` is assumed to be the entire key/value + input. + """ x_q = self.q_norm(x_q) - x_kv = self.kv_norm(x_kv) - return self.attention(x_q, x_kv, pad_mask=pad_mask, attn_mask=attn_mask) + + if x_kv is None: + x_kv_prefix = self.kv_norm(x_kv_prefix) + x_kv = torch.cat([x_kv_prefix, x_q], dim=1) + else: + x_kv = self.kv_norm(x_kv) + + return self.attention(x_q, x_kv, pad_mask=pad_mask, rot_pos_emb_q=rot_pos_emb_q, rot_pos_emb_k=rot_pos_emb_k) class SelfAttention(nn.Module): @@ -134,9 +178,12 @@ def __init__( num_channels: int, num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, + causal_attention: bool = False, dropout: float = 0.0, + qkv_bias: bool = True, + out_bias: bool = True, ): - """Multi-head self-attention (see `MultiHeadAttention` and for details).""" + """Pre-layer norm self-attention (see `MultiHeadAttention` and for attention details).""" super().__init__() self.norm = nn.LayerNorm(num_channels) self.attention = MultiHeadAttention( @@ -145,13 +192,16 @@ def __init__( num_kv_input_channels=num_channels, num_qk_channels=num_qk_channels, num_v_channels=num_v_channels, + causal_attention=causal_attention, dropout=dropout, + qkv_bias=qkv_bias, + out_bias=out_bias, ) - def forward(self, x, pad_mask=None, attn_mask=None): - """Multi-head attention of input `x` to itself after applying layer normalization to the input.""" + def forward(self, x, pad_mask=None, rot_pos_emb=None): + """Pre-layer norm self-attention of input `x`.""" x = self.norm(x) - return self.attention(x, x, pad_mask=pad_mask, attn_mask=attn_mask) + return self.attention(x, x, pad_mask=pad_mask, rot_pos_emb_q=rot_pos_emb, rot_pos_emb_k=rot_pos_emb) class CrossAttentionLayer(Sequential): @@ -162,9 +212,13 @@ def __init__( num_kv_input_channels: int, num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, + causal_attention: bool = False, widening_factor: int = 1, dropout: float = 0.0, attention_residual: bool = True, + qkv_bias: bool = True, + out_bias: bool = True, + mlp_bias: bool = True, ): cross_attn = CrossAttention( num_heads=num_heads, @@ -172,11 +226,14 @@ def __init__( num_kv_input_channels=num_kv_input_channels, num_qk_channels=num_qk_channels, num_v_channels=num_v_channels, + causal_attention=causal_attention, dropout=dropout, + qkv_bias=qkv_bias, + out_bias=out_bias, ) super().__init__( Residual(cross_attn) if attention_residual else cross_attn, - Residual(MLP(num_q_input_channels, widening_factor)), + Residual(MLP(num_q_input_channels, widening_factor, bias=mlp_bias)), ) @@ -187,19 +244,26 @@ def __init__( num_channels: int, num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, + causal_attention: bool = False, widening_factor: int = 1, dropout: float = 0.0, + qkv_bias: bool = True, + out_bias: bool = True, + mlp_bias: bool = True, ): self_attn = SelfAttention( num_heads=num_heads, num_channels=num_channels, num_qk_channels=num_qk_channels, num_v_channels=num_v_channels, + causal_attention=causal_attention, dropout=dropout, + qkv_bias=qkv_bias, + out_bias=out_bias, ) super().__init__( Residual(self_attn), - Residual(MLP(num_channels, widening_factor)), + Residual(MLP(num_channels, widening_factor, bias=mlp_bias)), ) @@ -211,10 +275,14 @@ def __init__( num_channels: int, num_qk_channels: Optional[int] = None, num_v_channels: Optional[int] = None, + causal_attention: bool = False, widening_factor: int = 1, dropout: float = 0.0, activation_checkpointing: bool = False, activation_offloading: bool = False, + qkv_bias: bool = True, + out_bias: bool = True, + mlp_bias: bool = True, ): layers = [ SelfAttentionLayer( @@ -222,8 +290,12 @@ def __init__( num_channels=num_channels, num_qk_channels=num_qk_channels, num_v_channels=num_v_channels, + causal_attention=causal_attention, widening_factor=widening_factor, dropout=dropout, + qkv_bias=qkv_bias, + out_bias=out_bias, + mlp_bias=mlp_bias, ) for _ in range(num_layers) ] @@ -235,24 +307,15 @@ def __init__( class MLP(Sequential): - def __init__(self, num_channels: int, widening_factor: int): + def __init__(self, num_channels: int, widening_factor: int, bias: bool = True): super().__init__( nn.LayerNorm(num_channels), - nn.Linear(num_channels, widening_factor * num_channels), + nn.Linear(num_channels, widening_factor * num_channels, bias=bias), nn.GELU(), - nn.Linear(widening_factor * num_channels, num_channels), + nn.Linear(widening_factor * num_channels, num_channels, bias=bias), ) -class Residual(nn.Module): - def __init__(self, module: nn.Module): - super().__init__() - self.module = module - - def forward(self, *args, **kwargs): - return self.module(*args, **kwargs) + args[0] - - class InputAdapter(nn.Module): def __init__(self, num_input_channels: int): """Transforms and position-encodes task-specific input to generic encoder input. @@ -270,6 +333,19 @@ def forward(self, x): raise NotImplementedError() +class RotarySupport(InputAdapter): + def __init__(self, encoded_channels_per_head: int, *args, **kwargs): + """An input adapter mixin that additionally generates constructor arguments for + `RotaryPositionEmbedding`.""" + super().__init__(*args, **kwargs) + self.frq_pos_encoding = FrequencyPositionEncoding(encoded_channels_per_head=encoded_channels_per_head) + + def forward(self, x): + """Transforms and position-encodes sequence `x` and additionally returns a frequency position encoding of + `x` required to create a `RotaryPositionEmbedding` instance.""" + return super().forward(x), self.frq_pos_encoding(x.shape[1]) + + class OutputAdapter(nn.Module): def __init__(self, output_query: Tensor, init_scale: float): """Transforms generic decoder cross-attention output to task-specific output. @@ -294,25 +370,6 @@ def output_query(self, x): return repeat(self._output_query, "... -> b ...", b=x.shape[0]) -class ClassificationOutputAdapter(OutputAdapter): - def __init__( - self, - num_classes: int, - num_output_queries: int = 1, - num_output_query_channels: Optional[int] = None, - init_scale: float = 0.02, - ): - - if num_output_query_channels is None: - num_output_query_channels = num_classes - - super().__init__(output_query=torch.empty(num_output_queries, num_output_query_channels), init_scale=init_scale) - self.linear = nn.Linear(num_output_query_channels, num_classes) - - def forward(self, x): - return self.linear(x).squeeze(dim=1) - - class PerceiverEncoder(nn.Module): def __init__( self, @@ -431,7 +488,7 @@ def self_attn(): def _init_parameters(self, init_scale: float): with torch.no_grad(): self.latent.normal_(0.0, init_scale) - _init_parameters(self, init_scale) + init_parameters(self, init_scale) @property def extra_cross_attention_layer(self): @@ -450,7 +507,7 @@ def forward(self, x, pad_mask=None): # repeat initial latent vector along batch dimension x_latent = repeat(self.latent, "... -> b ...", b=b) - x_latent = self.cross_attn_1(x_latent, x, pad_mask) + x_latent = self.cross_attn_1(x_latent, x, pad_mask=pad_mask) x_latent = self.self_attn_1(x_latent) cross_attn_n = self.cross_attn_n if self.extra_cross_attention_layer else self.cross_attn_1 @@ -458,7 +515,7 @@ def forward(self, x, pad_mask=None): for i in range(1, self.num_self_attention_blocks): if i < self.num_cross_attention_layers: - x_latent = cross_attn_n(x_latent, x, pad_mask) + x_latent = cross_attn_n(x_latent, x, pad_mask=pad_mask) x_latent = self_attn_n(x_latent) return x_latent @@ -518,7 +575,7 @@ def __init__( def _init_parameters(self, init_scale: float): with torch.no_grad(): - _init_parameters(self, init_scale) + init_parameters(self, init_scale) def forward(self, x, **kwargs): output_query = self.output_adapter.output_query(x) @@ -539,11 +596,127 @@ def decoder(self): return self[1] -def _init_parameters(module, init_scale): - for m in module.modules(): - if isinstance(m, nn.Linear): - m.weight.data.normal_(mean=0.0, std=init_scale) - if m.bias is not None: - m.bias.data.zero_() - elif isinstance(m, nn.Embedding): - m.weight.data.normal_(mean=0.0, std=init_scale) +class PerceiverAR(nn.Module): + def __init__( + self, + input_adapter: RotarySupport, + output_layer: nn.Module, + num_latents: int, + num_heads: int = 8, + num_self_attention_layers: int = 6, + cross_attention_widening_factor: int = 4, + self_attention_widening_factor: int = 4, + cross_attention_dropout: float = 0.5, + post_attention_dropout: float = 0.0, + init_scale: float = 0.02, + activation_checkpointing: bool = False, + activation_offloading: bool = False, + ): + """Experimental implementation of Perceiver AR (https://arxiv.org/abs/2202.07765). + + :param input_adapter: Transforms an input sequence to generic Perceiver AR input. An input adapter may choose + to add (absolute) position information to transformed inputs while `PerceiverAR` additionally computes a + rotary position embedding (i.e. relative position information) for queries and keys. To support the + computation of rotary position embeddings, concrete input adapters need to mixin `RotarySupport`. + :param output_layer: Transforms latent variables to task-specific output. This is usually a layer that predicts + the logits of a target sequence. + :param num_latents: Number of latent variables. + :param num_heads: Number of cross- and self-attention heads. + :param num_self_attention_layers: Number of self-attention layers. + :param cross_attention_dropout: Probability of dropping positions in the prefix sequence. + :param post_attention_dropout: Probability of dropping cross- and self-attention scores. + :param init_scale: Standard deviation for random normal initialization of parameters. + :param activation_checkpointing: If True, implements an activation checkpoint for each self-attention + layer and cross-attention layer. + :param activation_offloading: If True, offloads checkpointed activations to CPU. + """ + super().__init__() + + def cross_attn(): + layer = CrossAttentionLayer( + num_heads=num_heads, + num_q_input_channels=input_adapter.num_input_channels, + num_kv_input_channels=input_adapter.num_input_channels, + causal_attention=True, + widening_factor=cross_attention_widening_factor, + dropout=post_attention_dropout, + qkv_bias=False, + out_bias=True, + mlp_bias=False, + ) + return ( + checkpoint_wrapper(layer, offload_to_cpu=activation_offloading) if activation_checkpointing else layer + ) + + def self_attn(): + return SelfAttentionBlock( + num_layers=num_self_attention_layers, + num_heads=num_heads, + num_channels=input_adapter.num_input_channels, + causal_attention=True, + widening_factor=self_attention_widening_factor, + dropout=post_attention_dropout, + activation_checkpointing=activation_checkpointing, + activation_offloading=activation_offloading, + qkv_bias=False, + out_bias=False, + mlp_bias=False, + ) + + self.num_latents = num_latents + + self.input_adapter = input_adapter + self.output_layer = output_layer + + self.cross_attention_dropout = cross_attention_dropout + self.cross_attention = cross_attn() + self.self_attention = self_attn() + + self._init_parameters(init_scale) + + def _init_parameters(self, init_scale: float): + with torch.no_grad(): + init_parameters(self, init_scale) + + def forward(self, x): + x, frq_pos_enc = self.input_adapter(x) + + frq_pos_enc_q = frq_pos_enc + frq_pos_enc_k = frq_pos_enc + + x_latent = x[:, -self.num_latents :] + x_prefix = x[:, : -self.num_latents] + n_prefix = x_prefix.shape[1] + + b, n, _ = x.shape + + if self.training and self.cross_attention_dropout > 0.0: + rand = torch.rand(b, n_prefix, device=x.device) + # number of positions in prefix sequence to keep + keep = n_prefix - int(n_prefix * self.cross_attention_dropout) + # indices of positions in prefix sequence to keep + keep_indices = rand.topk(keep, dim=-1).indices + # mask of positions in prefix sequence to keep + keep_mask = torch.zeros_like(rand, dtype=torch.bool).scatter_(dim=1, index=keep_indices, value=1) + # drop positions in prefix sequence according to prefix_dropout + x_prefix = rearrange(x_prefix[keep_mask], "(b n) c -> b n c", b=b) + + frq_pos_enc_k = repeat(frq_pos_enc_k, "... -> b ...", b=b) + frq_pos_enc_k_latent = frq_pos_enc_k[:, n_prefix:] + frq_pos_enc_prefix = frq_pos_enc_k[:, :n_prefix] + frq_pos_enc_prefix = rearrange(frq_pos_enc_prefix[keep_mask], "(b n) c -> b n c", b=b) + + frq_pos_enc_k = torch.cat((frq_pos_enc_prefix, frq_pos_enc_k_latent), dim=1) + frq_pos_enc_k = rearrange(frq_pos_enc_k, "b n c -> b 1 n c") + + x_latent = self.cross_attention( + x_latent, + x_kv_prefix=x_prefix, + rot_pos_emb_q=RotaryPositionEmbedding(frq_pos_enc_q, right_align=True), + rot_pos_emb_k=RotaryPositionEmbedding(frq_pos_enc_k, right_align=True), + ) + + x_latent = self.self_attention(x_latent, rot_pos_emb=RotaryPositionEmbedding(frq_pos_enc, right_align=True)) + x_logits = self.output_layer(x_latent) + + return x_logits diff --git a/perceiver/model/core/position.py b/perceiver/model/core/position.py new file mode 100644 index 0000000..e1a609f --- /dev/null +++ b/perceiver/model/core/position.py @@ -0,0 +1,45 @@ +import torch +import torch.nn as nn + + +class RotaryPositionEmbedding: + # See section 3.4.2 in https://arxiv.org/abs/2104.09864 + # (here, a different permutation of channels is used) + + def __init__(self, frq_pos_enc: torch.Tensor, right_align: bool = False): + # frq_pos_enc shape is either (n, c) or (b, 1, n, c). + # frq_pos_enc is broadcast to (b, h, n, c). + self.frq_pos_enc = frq_pos_enc + self.rotate_dim = frq_pos_enc.shape[-1] + self.right_align = right_align + + def rotate(self, t): + seq_len = t.shape[-2] + if self.right_align: + # q and k are right-aligned in Perceiver AR + pos_enc = self.frq_pos_enc[..., -seq_len:, :] + else: + # q and k are left-aligned + pos_enc = self.frq_pos_enc[..., :seq_len, :] + + t_rot, t_pass = t[..., : self.rotate_dim], t[..., self.rotate_dim :] + t_rot = (t_rot * pos_enc.cos()) + (self._rotate_half(t_rot) * pos_enc.sin()) + + return torch.cat((t_rot, t_pass), dim=-1) + + def _rotate_half(self, x): + x1 = x[..., : self.rotate_dim // 2] + x2 = x[..., self.rotate_dim // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +class FrequencyPositionEncoding(nn.Module): + def __init__(self, encoded_channels_per_head): + super().__init__() + inv_freq = 1.0 / (10000 ** (torch.arange(0, encoded_channels_per_head, 2).float() / encoded_channels_per_head)) + self.register_buffer("inv_freq", inv_freq) + + def forward(self, seq_len): + pos = torch.arange(seq_len, dtype=self.inv_freq.dtype, device=self.inv_freq.device) + pos_enc = torch.outer(pos, self.inv_freq) + return torch.cat((pos_enc, pos_enc), dim=-1) diff --git a/perceiver/model/core/utils.py b/perceiver/model/core/utils.py index 7caf407..63f981a 100644 --- a/perceiver/model/core/utils.py +++ b/perceiver/model/core/utils.py @@ -4,19 +4,42 @@ class Sequential(nn.Sequential): - def forward(self, *x): - for module in self: + def forward(self, *x, **kwargs): + for i, module in enumerate(self): if type(x) == tuple: - x = module(*x) + if i == 0: + x = module(*x, **kwargs) + else: + x = module(*x) else: x = module(x) return x +class Residual(nn.Module): + def __init__(self, module: nn.Module): + super().__init__() + self.module = module + + def forward(self, *args, **kwargs): + return self.module(*args, **kwargs) + args[0] + + +def init_parameters(module, init_scale): + for m in module.modules(): + if isinstance(m, nn.Linear): + m.weight.data.normal_(mean=0.0, std=init_scale) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.Embedding): + m.weight.data.normal_(mean=0.0, std=init_scale) + + def freeze(module: nn.Module): for param in module.parameters(): param.requires_grad = False def is_checkpoint(path: str): + # TODO: provide a more robust implementation return os.path.splitext(path)[1] == ".ckpt" diff --git a/perceiver/model/image/classifier.py b/perceiver/model/image/classifier.py index 67e957c..531fca8 100644 --- a/perceiver/model/image/classifier.py +++ b/perceiver/model/image/classifier.py @@ -10,8 +10,6 @@ from transformers import PerceiverConfig as HuggingfacePerceiverConfig, PerceiverForImageClassificationFourier from perceiver.model.core import ( - ClassificationDecoderConfig, - ClassificationOutputAdapter, EncoderConfig, InputAdapter, LitClassifier, @@ -20,6 +18,7 @@ PerceiverEncoder, PerceiverIO, ) +from perceiver.model.core.classifier import ClassificationDecoderConfig, ClassificationOutputAdapter from perceiver.model.core.convert import ( copy_cross_attention_layer_params, copy_param, diff --git a/perceiver/model/text/classifier.py b/perceiver/model/text/classifier.py index b6e9ded..6adc923 100644 --- a/perceiver/model/text/classifier.py +++ b/perceiver/model/text/classifier.py @@ -1,17 +1,10 @@ from typing import Any -from perceiver.model.core import ( - ClassificationDecoderConfig, - ClassificationOutputAdapter, - LitClassifier, - PerceiverConfig, - PerceiverDecoder, - PerceiverIO, -) - +from perceiver.model.core import LitClassifier, PerceiverConfig, PerceiverDecoder, PerceiverIO +from perceiver.model.core.classifier import ClassificationDecoderConfig, ClassificationOutputAdapter from perceiver.model.core.utils import is_checkpoint from perceiver.model.text.common import TextEncoder, TextEncoderConfig -from perceiver.model.text.language import LitLanguageModel +from perceiver.model.text.mlm import LitMaskedLanguageModel class TextClassifier(PerceiverIO): @@ -61,7 +54,7 @@ def __init__(self, encoder: TextEncoderConfig, decoder: ClassificationDecoderCon lit_model = LitTextClassifier.load_from_checkpoint(model_params, params=None) self.model.load_state_dict(lit_model.model.state_dict()) if encoder_params is not None and is_checkpoint(encoder_params): - lit_model = LitLanguageModel.load_from_checkpoint(encoder_params, params=None) + lit_model = LitMaskedLanguageModel.load_from_checkpoint(encoder_params, params=None) self.model.encoder.load_state_dict(lit_model.model.encoder.state_dict()) def forward(self, batch): diff --git a/perceiver/model/text/clm.py b/perceiver/model/text/clm.py new file mode 100644 index 0000000..e8c50b8 --- /dev/null +++ b/perceiver/model/text/clm.py @@ -0,0 +1,214 @@ +from dataclasses import dataclass +from typing import Any, Optional + +import pytorch_lightning as pl +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from pytorch_lightning.loggers import TensorBoardLogger + +from perceiver.model.core import PerceiverAR, RotarySupport +from perceiver.model.core.config import base_kwargs +from perceiver.model.text import common + + +@dataclass +class CausalLanguageModelConfig: + vocab_size: int + max_seq_len: int + num_latents: int + num_channels: int + num_heads: int = 8 + num_self_attention_layers: int = 8 + widening_factor: int = 4 + cross_attention_dropout: float = 0.5 + post_attention_dropout: float = 0.0 + random_sequence_truncation: bool = False + init_scale: float = 0.02 + activation_checkpointing: bool = False + activation_offloading: bool = False + + def base_kwargs(self, exclude=()): + return base_kwargs(self, CausalLanguageModelConfig, exclude) + + +class TextInputAdapter(RotarySupport, common.TextInputAdapter): + def __init__(self, *args, random_sequence_truncation: bool = False, **kwargs): + super().__init__(*args, **kwargs) + self.random_sequence_truncation = random_sequence_truncation + + def forward(self, x): + if self.random_sequence_truncation and self.training: + # TODO: consider moving random truncation to data loaders + # (and make it working properly with distributed training) + + # Alternative to (or combination with) cross-attention dropout + n = torch.randint(16, self.max_seq_len + 1, (1,)).to(x.device) + x = x[:, -n:] # right-alignment with labels from data source + return super().forward(x) + + +class CausalLanguageModel(PerceiverAR): + def __init__(self, config: CausalLanguageModelConfig): + input_adapter = TextInputAdapter( + # Compute rotary position embedding for 50% of channels only ... + encoded_channels_per_head=config.num_channels // config.num_heads // 2, + random_sequence_truncation=config.random_sequence_truncation, + vocab_size=config.vocab_size, + max_seq_len=config.max_seq_len, + num_input_channels=config.num_channels, + init_scale=config.init_scale, + ) + output_layer = nn.Linear(config.num_channels, config.vocab_size, bias=False) + super().__init__( + input_adapter=input_adapter, + output_layer=output_layer, + num_latents=config.num_latents, + num_heads=config.num_heads, + num_self_attention_layers=config.num_self_attention_layers, + cross_attention_widening_factor=config.widening_factor, + self_attention_widening_factor=config.widening_factor, + cross_attention_dropout=config.cross_attention_dropout, + post_attention_dropout=config.post_attention_dropout, + init_scale=config.init_scale, + activation_checkpointing=config.activation_checkpointing, + activation_offloading=config.activation_offloading, + ) + + @torch.no_grad() + def generate(self, num: int, prompt: torch.Tensor, threshold: float = 0.9, temperature: float = 1.0): + """Generate sequence from `prompt` via top-k sampling (with k determined by `threshold`) at given + `temperature`.""" + + # TODO: support pad and eos, usually needed for batch sizes > 1 at inference time. + _, n = prompt.shape + result = prompt + + for _ in range(num): + logits = self(result[:, -self.input_adapter.max_seq_len :])[:, -1] + logits = self.top_f(logits, fraction=1 - threshold) + probs = F.softmax(logits / temperature, dim=-1) + sample = torch.multinomial(probs, 1) + result = torch.cat((result, sample), dim=-1) + + return result[:, n:] + + @staticmethod + def top_f(logits: torch.Tensor, fraction: float = 0.1): + """Keep the highest `fraction` of elements in `logits` and set others to `-inf`.""" + k = int(fraction * logits.shape[-1]) + val, idx = torch.topk(logits, k) + logits_top = torch.full_like(logits, float("-inf")) + logits_top.scatter_(1, idx, val) + return logits_top + + +class LitCausalLanguageModel(pl.LightningModule): + def __init__( + self, + vocab_size: int, + max_seq_len: int, + num_latents: int, + num_channels: int, + num_heads: int = 8, + num_self_attention_layers: int = 6, + widening_factor: int = 4, + cross_attention_dropout: float = 0.5, + post_attention_dropout: float = 0.0, + random_sequence_truncation: bool = False, + init_scale: float = 0.02, + activation_checkpointing=False, + activation_offloading=False, + validation_sample_prompt: Optional[str] = None, + validation_sample_record: Optional[int] = None, + ): + super().__init__() + self.save_hyperparameters() + self.model = CausalLanguageModel( + CausalLanguageModelConfig( + vocab_size=vocab_size, + max_seq_len=max_seq_len, + num_latents=num_latents, + num_channels=num_channels, + num_heads=num_heads, + num_self_attention_layers=num_self_attention_layers, + widening_factor=widening_factor, + cross_attention_dropout=cross_attention_dropout, + post_attention_dropout=post_attention_dropout, + random_sequence_truncation=random_sequence_truncation, + init_scale=init_scale, + activation_checkpointing=activation_checkpointing, + activation_offloading=activation_offloading, + ) + ) + self.loss = nn.CrossEntropyLoss() + + @classmethod + def create(cls, config: CausalLanguageModelConfig, **kwargs: Any): + return cls(**config.base_kwargs(), **kwargs) + + def setup(self, stage: Optional[str] = None): + dm = self.trainer.datamodule + self.preprocessor = dm.text_preprocessor() + self.tokenizer = dm.tokenizer + self.ds_valid = dm.ds_valid + + def forward(self, x): + return self.model(x) + + def step(self, batch): + labels, x, _ = batch + logits = self(x) + logits = rearrange(logits, "b n c -> b c n") + return self.loss(logits, labels[:, -logits.shape[2] :]) + + def training_step(self, batch, batch_idx): + loss = self.step(batch) + self.log("train_loss", loss) + return loss + + def validation_step(self, batch, batch_idx): + loss = self.step(batch) + self.log("val_loss", loss, prog_bar=True, sync_dist=True) + + def on_validation_epoch_end(self) -> None: + if self.global_rank == 0: + if self.hparams.validation_sample_record is not None: + if self.hparams.validation_sample_record == -1: + # pick a random record from ds_valid as prompt + record_idx = torch.randint(len(self.ds_valid), (1,)).item() + else: + # pick the specified record from ds_valid as prompt + record_idx = self.hparams.validation_sample_record + + prompt = self.ds_valid[record_idx]["input_ids"] + prompt_text = self.tokenizer.decode(prompt) + prompt = torch.tensor(prompt).to(self.device) + + result = self.model.generate(num=512, prompt=prompt[None, ...], threshold=0.9) + result_text = self.tokenizer.decode(result[0]) + + self.log_sample(tag="generated text (1)", prompt=prompt_text, generated=result_text) + + if self.hparams.validation_sample_prompt is not None: + prompt_text = self.hparams.validation_sample_prompt + prompt, _ = self.preprocessor.preprocess(prompt_text) + prompt = prompt.to(self.device) + + result = self.model.generate(num=512, prompt=prompt[None, ...], threshold=0.9) + result_text = self.tokenizer.decode(result[0]) + + self.log_sample(tag="generated text (2)", prompt=prompt_text, generated=result_text) + + def log_sample(self, tag, prompt, generated): + if isinstance(self.logger, TensorBoardLogger): + text = f"prompt: {cleanup(prompt)}\n" f"generated: {cleanup(generated)}\n" + self.logger.experiment.add_text(tag, f"

{text}
", self.trainer.global_step) + else: + # support other loggers here ... + ... + + +def cleanup(text): + return "".join([chr(max(32, ord(c))) for c in text]) diff --git a/perceiver/model/text/common.py b/perceiver/model/text/common.py index 75196a3..7462eec 100644 --- a/perceiver/model/text/common.py +++ b/perceiver/model/text/common.py @@ -37,10 +37,17 @@ def _init_parameters(self, init_scale: float): with torch.no_grad(): self.pos_encoding.normal_(0.0, init_scale) + @property + def vocab_size(self): + return self.txt_embedding.num_embeddings + + @property + def max_seq_len(self): + return self.pos_encoding.shape[0] + def forward(self, x): - b, l = x.shape # noqa: E741 - # FIXME: make compatible with left-truncated sequences - p_enc = rearrange(self.pos_encoding[:l], "... -> () ...") + _, n = x.shape + p_enc = rearrange(self.pos_encoding[:n], "... -> () ...") return self.txt_embedding(x) + p_enc diff --git a/perceiver/model/text/language.py b/perceiver/model/text/language.py index 83c4074..45b8b9a 100644 --- a/perceiver/model/text/language.py +++ b/perceiver/model/text/language.py @@ -1,218 +1,2 @@ -import os -from dataclasses import dataclass -from typing import Any, List, Optional - -import torch -import torch.nn as nn -from einops import rearrange -from transformers import PerceiverConfig as HuggingfacePerceiverConfig, PerceiverForMaskedLM - -from perceiver.model.core import DecoderConfig, LitModel, OutputAdapter, PerceiverConfig, PerceiverDecoder, PerceiverIO -from perceiver.model.core.convert import copy_cross_attention_layer_params -from perceiver.model.core.utils import is_checkpoint -from perceiver.model.text.common import copy_encoder_params, TextEncoder, TextEncoderConfig -from perceiver.model.text.utils import MaskedSamplePrediction - - -@dataclass -class TextDecoderConfig(DecoderConfig): - num_output_query_channels: Optional[int] = None - vocab_size: int = 10003 - max_seq_len: int = 512 - - -class TextOutputAdapter(OutputAdapter): - def __init__( - self, - vocab_size: int, - max_seq_len: int, - num_output_query_channels: int, - init_scale: float = 0.02, - ): - super().__init__(output_query=torch.empty(max_seq_len, num_output_query_channels), init_scale=init_scale) - self.linear = nn.Linear(num_output_query_channels, vocab_size) - - def forward(self, x): - return self.linear(x).squeeze(dim=1) - - -class TiedTextOutputAdapter(OutputAdapter): - def __init__(self, max_seq_len: int, vocab_size: int, num_input_channels: int, init_scale: float = 0.02): - super().__init__(output_query=torch.empty(max_seq_len, num_input_channels), init_scale=init_scale) - self.bias = nn.Parameter(torch.zeros(vocab_size)) - - def forward(self, x, txt_embedding: nn.Embedding): - return torch.matmul(x, txt_embedding.weight.T) + self.bias - - -class LanguageModel(PerceiverIO): - def __init__(self, config: PerceiverConfig[TextEncoderConfig, TextDecoderConfig]): - encoder = TextEncoder( - config.encoder, - num_latents=config.num_latents, - num_latent_channels=config.num_latent_channels, - activation_checkpointing=config.activation_checkpointing, - activation_offloading=config.activation_offloading, - ) - if config.decoder.num_output_query_channels is None: - output_adapter = TiedTextOutputAdapter( - max_seq_len=config.decoder.max_seq_len, - vocab_size=config.decoder.vocab_size, - num_input_channels=config.encoder.num_input_channels, - init_scale=config.decoder.init_scale, - ) - else: - output_adapter = TextOutputAdapter( - vocab_size=config.decoder.vocab_size, - max_seq_len=config.decoder.max_seq_len, - num_output_query_channels=config.decoder.num_output_query_channels, - init_scale=config.decoder.init_scale, - ) - decoder = PerceiverDecoder( - output_adapter=output_adapter, - num_latent_channels=config.num_latent_channels, - activation_checkpointing=config.activation_checkpointing, - activation_offloading=config.activation_offloading, - **config.decoder.base_kwargs() - ) - super().__init__(encoder, decoder) - - if config.params is None or is_checkpoint(config.params): - pass - elif os.path.isfile(config.params): - self.load_state_dict(torch.load(config.params)) - else: - # import model params from Huggingface Perceiver - model = PerceiverForMaskedLM.from_pretrained(config.params) - copy_encoder_params(model, self.encoder) - copy_decoder_params(model, self.decoder) - - def forward(self, x_masked, pad_mask=None, masking=True): - _, l = x_masked.shape # noqa: E741 - - x_latent = self.encoder(x_masked, pad_mask) - if isinstance(self.decoder.output_adapter, TiedTextOutputAdapter): - x_logits = self.decoder(x_latent, txt_embedding=self.encoder.input_adapter.txt_embedding) - else: - x_logits = self.decoder(x_latent) - - # FIXME: make compatible with left-truncated sequences - return x_logits[:, :l, :] - - -class LitLanguageModel(MaskedSamplePrediction, LitModel): - def __init__( - self, - encoder: TextEncoderConfig, - decoder: TextDecoderConfig, - *args: Any, - # TODO: investigate why the following two params must - # be redundantly added here after upgrading to - # jsonargparse 4.12.0 (from 4.7.*). - num_predictions: int = 3, - masked_samples: Optional[List[str]] = None, - **kwargs: Any - ): - super().__init__( - encoder, decoder, *args, num_predictions=num_predictions, masked_samples=masked_samples, **kwargs - ) - self.model = LanguageModel( - PerceiverConfig( - encoder=encoder, - decoder=decoder, - num_latents=self.hparams.num_latents, - num_latent_channels=self.hparams.num_latent_channels, - activation_checkpointing=self.hparams.activation_checkpointing, - activation_offloading=self.hparams.activation_offloading, - params=self.hparams.params, - ) - ) - self.loss = nn.CrossEntropyLoss() - - if self.hparams.params is not None and is_checkpoint(self.hparams.params): - lit_model = LitLanguageModel.load_from_checkpoint(self.hparams.params, params=None) - self.model.load_state_dict(lit_model.model.state_dict()) - - def forward(self, x, pad_mask): - return self.model(x, pad_mask) - - def step(self, batch): - labels, x, pad_mask = batch - logits = self(x, pad_mask) - logits = rearrange(logits, "b n c -> b c n") - return self.loss(logits, labels) - - def training_step(self, batch, batch_idx): - loss = self.step(batch) - self.log("train_loss", loss) - return loss - - def validation_step(self, batch, batch_idx): - loss = self.step(batch) - self.log("val_loss", loss, prog_bar=True) - - def test_step(self, batch, batch_idx): - loss = self.step(batch) - self.log("test_loss", loss) - - -def copy_output_adapter_params(src: PerceiverForMaskedLM, tgt: TiedTextOutputAdapter): - bias_src = src.embedding_decoder.bias - bias_tgt = tgt.bias - - with torch.no_grad(): - bias_tgt.copy_(bias_src) - - query_src = src.perceiver.decoder.output_position_encodings.position_embeddings - query_tgt = tgt._output_query - - with torch.no_grad(): - query_tgt.copy_(query_src) - - -def copy_decoder_params(src: PerceiverForMaskedLM, tgt: PerceiverDecoder): - copy_cross_attention_layer_params( - src.perceiver.decoder.decoding_cross_attention, tgt.cross_attn, query_residual=False - ) - copy_output_adapter_params(src, tgt.output_adapter) - - -def convert_config(config: HuggingfacePerceiverConfig) -> PerceiverConfig[TextEncoderConfig, TextDecoderConfig]: - assert config.hidden_act == "gelu" - assert config.tie_word_embeddings - - encoder_config = TextEncoderConfig( - vocab_size=config.vocab_size, - max_seq_len=config.max_position_embeddings, - num_input_channels=config.d_model, - num_cross_attention_qk_channels=config.qk_channels, - num_cross_attention_v_channels=config.v_channels, - num_cross_attention_heads=config.num_cross_attention_heads, - num_self_attention_qk_channels=config.qk_channels, - num_self_attention_v_channels=config.v_channels, - num_self_attention_heads=config.num_self_attention_heads, - num_self_attention_layers_per_block=config.num_self_attends_per_block, - num_self_attention_blocks=config.num_blocks, - cross_attention_widening_factor=config.cross_attention_widening_factor, - self_attention_widening_factor=config.self_attention_widening_factor, - dropout=config.attention_probs_dropout_prob, - init_scale=config.initializer_range, - ) - decoder_config = TextDecoderConfig( - vocab_size=config.vocab_size, - max_seq_len=config.max_position_embeddings, - num_cross_attention_qk_channels=config.qk_channels, - num_cross_attention_v_channels=config.d_model, - num_cross_attention_heads=config.num_cross_attention_heads, - cross_attention_widening_factor=config.cross_attention_widening_factor, - cross_attention_residual=False, - dropout=config.attention_probs_dropout_prob, - init_scale=config.initializer_range, - ) - return PerceiverConfig( - encoder_config, - decoder_config, - num_latents=config.num_latents, - num_latent_channels=config.d_latents, - params=config.name_or_path, - ) +# For backwards compatibility only +from perceiver.model.text.mlm import * # noqa: F401, F403 diff --git a/perceiver/model/text/mlm.py b/perceiver/model/text/mlm.py new file mode 100644 index 0000000..91a5336 --- /dev/null +++ b/perceiver/model/text/mlm.py @@ -0,0 +1,258 @@ +import html +import os +from dataclasses import dataclass +from typing import Any, List, Optional + +import torch +import torch.nn as nn +from einops import rearrange +from pytorch_lightning.loggers import TensorBoardLogger +from transformers import PerceiverConfig as HuggingfacePerceiverConfig, PerceiverForMaskedLM + +from perceiver.model.core import DecoderConfig, LitModel, OutputAdapter, PerceiverConfig, PerceiverDecoder, PerceiverIO +from perceiver.model.core.convert import copy_cross_attention_layer_params +from perceiver.model.core.utils import is_checkpoint +from perceiver.model.text.common import copy_encoder_params, TextEncoder, TextEncoderConfig + + +@dataclass +class TextDecoderConfig(DecoderConfig): + num_output_query_channels: Optional[int] = None + vocab_size: int = 10003 + max_seq_len: int = 512 + + +class TextOutputAdapter(OutputAdapter): + def __init__( + self, + vocab_size: int, + max_seq_len: int, + num_output_query_channels: int, + init_scale: float = 0.02, + ): + super().__init__(output_query=torch.empty(max_seq_len, num_output_query_channels), init_scale=init_scale) + self.linear = nn.Linear(num_output_query_channels, vocab_size) + + def forward(self, x): + return self.linear(x).squeeze(dim=1) + + +class TiedTextOutputAdapter(OutputAdapter): + def __init__(self, max_seq_len: int, vocab_size: int, num_input_channels: int, init_scale: float = 0.02): + super().__init__(output_query=torch.empty(max_seq_len, num_input_channels), init_scale=init_scale) + self.bias = nn.Parameter(torch.zeros(vocab_size)) + + def forward(self, x, txt_embedding: nn.Embedding): + return torch.matmul(x, txt_embedding.weight.T) + self.bias + + +class MaskedLanguageModel(PerceiverIO): + def __init__(self, config: PerceiverConfig[TextEncoderConfig, TextDecoderConfig]): + encoder = TextEncoder( + config.encoder, + num_latents=config.num_latents, + num_latent_channels=config.num_latent_channels, + activation_checkpointing=config.activation_checkpointing, + activation_offloading=config.activation_offloading, + ) + if config.decoder.num_output_query_channels is None: + output_adapter = TiedTextOutputAdapter( + max_seq_len=config.decoder.max_seq_len, + vocab_size=config.decoder.vocab_size, + num_input_channels=config.encoder.num_input_channels, + init_scale=config.decoder.init_scale, + ) + else: + output_adapter = TextOutputAdapter( + vocab_size=config.decoder.vocab_size, + max_seq_len=config.decoder.max_seq_len, + num_output_query_channels=config.decoder.num_output_query_channels, + init_scale=config.decoder.init_scale, + ) + decoder = PerceiverDecoder( + output_adapter=output_adapter, + num_latent_channels=config.num_latent_channels, + activation_checkpointing=config.activation_checkpointing, + activation_offloading=config.activation_offloading, + **config.decoder.base_kwargs() + ) + super().__init__(encoder, decoder) + + if config.params is None or is_checkpoint(config.params): + pass + elif os.path.isfile(config.params): + self.load_state_dict(torch.load(config.params)) + else: + # import model params from Huggingface Perceiver + model = PerceiverForMaskedLM.from_pretrained(config.params) + copy_encoder_params(model, self.encoder) + copy_decoder_params(model, self.decoder) + + def forward(self, x_masked, pad_mask=None): + _, n = x_masked.shape + + x_latent = self.encoder(x_masked, pad_mask) + if isinstance(self.decoder.output_adapter, TiedTextOutputAdapter): + x_logits = self.decoder(x_latent, txt_embedding=self.encoder.input_adapter.txt_embedding) + else: + x_logits = self.decoder(x_latent) + + return x_logits[:, :n, :] + + +class LitMaskedLanguageModel(LitModel): + def __init__( + self, + encoder: TextEncoderConfig, + decoder: TextDecoderConfig, + *args: Any, + num_predictions: int = 3, + masked_samples: Optional[List[str]] = None, + **kwargs: Any + ): + super().__init__(encoder, decoder, *args, **kwargs) + self.model = MaskedLanguageModel( + PerceiverConfig( + encoder=encoder, + decoder=decoder, + num_latents=self.hparams.num_latents, + num_latent_channels=self.hparams.num_latent_channels, + activation_checkpointing=self.hparams.activation_checkpointing, + activation_offloading=self.hparams.activation_offloading, + params=self.hparams.params, + ) + ) + self.loss = nn.CrossEntropyLoss() + + if self.hparams.params is not None and is_checkpoint(self.hparams.params): + lit_model = LitMaskedLanguageModel.load_from_checkpoint(self.hparams.params, params=None) + self.model.load_state_dict(lit_model.model.state_dict()) + + def setup(self, stage: Optional[str] = None): + self.filler = MaskedSampleFiller(preprocessor=self.trainer.datamodule.text_preprocessor(), model=self) + + def forward(self, x, pad_mask): + return self.model(x, pad_mask) + + def step(self, batch): + labels, x, pad_mask = batch + logits = self(x, pad_mask) + logits = rearrange(logits, "b n c -> b c n") + return self.loss(logits, labels) + + def training_step(self, batch, batch_idx): + loss = self.step(batch) + self.log("train_loss", loss) + return loss + + def validation_step(self, batch, batch_idx): + loss = self.step(batch) + self.log("val_loss", loss, prog_bar=True, sync_dist=True) + + def test_step(self, batch, batch_idx): + loss = self.step(batch) + self.log("test_loss", loss, sync_dist=True) + + def on_validation_epoch_end(self) -> None: + if self.hparams.masked_samples: + masked_samples, filled_samples = self.filler.fill( + self.hparams.masked_samples, self.hparams.num_predictions, self.device + ) + + if isinstance(self.logger, TensorBoardLogger): + rendered_samples = "\n\n".join( + [" \n".join([html.escape(s)] + ps) for s, ps in zip(masked_samples, filled_samples)] + ) + self.logger.experiment.add_text("sample predictions", rendered_samples, self.trainer.global_step) + else: + # support other loggers here ... + ... + + +class MaskedSampleFiller: + def __init__(self, preprocessor, model=None): + self.preprocessor = preprocessor + self.model = model + + def fill(self, masked_samples, num_predictions, device="cpu"): + masked_samples = [ms.replace("", self.preprocessor.tokenizer.mask_token) for ms in masked_samples] + + xs, ms = self.preprocessor.preprocess_batch(masked_samples) + xs = xs.to(device) + ms = ms.to(device) + + with torch.no_grad(): + x_logits = self.model(xs, ms) + + pred_mask = xs == self.preprocessor.tokenizer.mask_token_id + pred_ids = torch.topk(x_logits[pred_mask, :], k=num_predictions, dim=1).indices + + results = [] + + for i in range(num_predictions): + xs[pred_mask] = pred_ids[:, i] + results.append(self.preprocessor.tokenizer.batch_decode(xs, skip_special_tokens=True)) + + return masked_samples, list(map(list, zip(*results))) # transpose results (a list of lists) + + +def copy_output_adapter_params(src: PerceiverForMaskedLM, tgt: TiedTextOutputAdapter): + bias_src = src.embedding_decoder.bias + bias_tgt = tgt.bias + + with torch.no_grad(): + bias_tgt.copy_(bias_src) + + query_src = src.perceiver.decoder.output_position_encodings.position_embeddings + query_tgt = tgt._output_query + + with torch.no_grad(): + query_tgt.copy_(query_src) + + +def copy_decoder_params(src: PerceiverForMaskedLM, tgt: PerceiverDecoder): + copy_cross_attention_layer_params( + src.perceiver.decoder.decoding_cross_attention, tgt.cross_attn, query_residual=False + ) + copy_output_adapter_params(src, tgt.output_adapter) + + +def convert_config(config: HuggingfacePerceiverConfig) -> PerceiverConfig[TextEncoderConfig, TextDecoderConfig]: + assert config.hidden_act == "gelu" + assert config.tie_word_embeddings + + encoder_config = TextEncoderConfig( + vocab_size=config.vocab_size, + max_seq_len=config.max_position_embeddings, + num_input_channels=config.d_model, + num_cross_attention_qk_channels=config.qk_channels, + num_cross_attention_v_channels=config.v_channels, + num_cross_attention_heads=config.num_cross_attention_heads, + num_self_attention_qk_channels=config.qk_channels, + num_self_attention_v_channels=config.v_channels, + num_self_attention_heads=config.num_self_attention_heads, + num_self_attention_layers_per_block=config.num_self_attends_per_block, + num_self_attention_blocks=config.num_blocks, + cross_attention_widening_factor=config.cross_attention_widening_factor, + self_attention_widening_factor=config.self_attention_widening_factor, + dropout=config.attention_probs_dropout_prob, + init_scale=config.initializer_range, + ) + decoder_config = TextDecoderConfig( + vocab_size=config.vocab_size, + max_seq_len=config.max_position_embeddings, + num_cross_attention_qk_channels=config.qk_channels, + num_cross_attention_v_channels=config.d_model, + num_cross_attention_heads=config.num_cross_attention_heads, + cross_attention_widening_factor=config.cross_attention_widening_factor, + cross_attention_residual=False, + dropout=config.attention_probs_dropout_prob, + init_scale=config.initializer_range, + ) + return PerceiverConfig( + encoder_config, + decoder_config, + num_latents=config.num_latents, + num_latent_channels=config.d_latents, + params=config.name_or_path, + ) diff --git a/perceiver/model/text/utils.py b/perceiver/model/text/utils.py deleted file mode 100644 index 013e029..0000000 --- a/perceiver/model/text/utils.py +++ /dev/null @@ -1,60 +0,0 @@ -import html -from typing import Any, List, Optional - -import pytorch_lightning as pl -import torch -from pytorch_lightning.loggers import TensorBoardLogger - - -class MaskedSamplePrediction(pl.LightningModule): - def __init__(self, *args: Any, masked_samples: Optional[List[str]] = None, num_predictions: int = 3, **kwargs: Any): - super().__init__(*args, **kwargs) - self.save_hyperparameters() - self.preprocessor = None - - def setup(self, stage: Optional[str] = None): - self.preprocessor = self.trainer.datamodule.text_preprocessor() - - def on_validation_epoch_end(self) -> None: - if self.hparams.masked_samples: - masked_samples, filled_samples = self.fill_masks(self.hparams.masked_samples, self.hparams.num_predictions) - - if isinstance(self.logger, TensorBoardLogger): - rendered_samples = "\n\n".join( - [" \n".join([html.escape(s)] + ps) for s, ps in zip(masked_samples, filled_samples)] - ) - self.logger.experiment.add_text("sample predictions", rendered_samples, self.trainer.global_step) - else: - # support other loggers here ... - ... - - def fill_masks(self, masked_samples, num_predictions): - masked_samples = [ms.replace("", self.preprocessor.tokenizer.mask_token) for ms in masked_samples] - - xs, ms = self.preprocessor.preprocess_batch(masked_samples) - xs = xs.to(self.device) - ms = ms.to(self.device) - - with torch.no_grad(): - x_logits = self(xs, ms) - - pred_mask = xs == self.preprocessor.tokenizer.mask_token_id - pred_ids = torch.topk(x_logits[pred_mask, :], k=num_predictions, dim=1).indices - - results = [] - - for i in range(num_predictions): - xs[pred_mask] = pred_ids[:, i] - results.append(self.preprocessor.tokenizer.batch_decode(xs, skip_special_tokens=True)) - - return masked_samples, list(map(list, zip(*results))) # transpose results (a list of lists) - - -class MaskedSamplePredictionUtil(MaskedSamplePrediction): - def __init__(self, preprocessor): - super().__init__() - self.preprocessor = preprocessor - self.model = None - - def forward(self, x, pad_mask=None): - return self.model(x, pad_mask) diff --git a/perceiver/scripts/text/__init__.py b/perceiver/scripts/text/__init__.py index cfa04bf..96e2b8f 100644 --- a/perceiver/scripts/text/__init__.py +++ b/perceiver/scripts/text/__init__.py @@ -2,6 +2,7 @@ from perceiver.data.text import ( BookCorpusDataModule, + Enwik8DataModule, ImdbDataModule, WikiBookDataModule, WikipediaDataModule, diff --git a/perceiver/scripts/text/clm.py b/perceiver/scripts/text/clm.py new file mode 100644 index 0000000..d71a2a8 --- /dev/null +++ b/perceiver/scripts/text/clm.py @@ -0,0 +1,24 @@ +from pytorch_lightning.utilities.cli import LightningArgumentParser + +from perceiver.model.text.clm import LitCausalLanguageModel +from perceiver.scripts.cli import CLI + + +class CausalLanguageModelCLI(CLI): + def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None: + super().add_arguments_to_parser(parser) + parser.link_arguments("data.max_seq_len", "model.max_seq_len", apply_on="instantiate") + parser.link_arguments("data.vocab_size", "model.vocab_size", apply_on="instantiate") + parser.set_defaults( + { + "model.num_latents": 512, + "model.num_channels": 512, + "model.num_self_attention_layers": 8, + "model.cross_attention_dropout": 0.5, + "model.post_attention_dropout": 0.0, + } + ) + + +if __name__ == "__main__": + CausalLanguageModelCLI(LitCausalLanguageModel, description="Causal language model", run=True) diff --git a/perceiver/scripts/text/lm.py b/perceiver/scripts/text/mlm.py similarity index 94% rename from perceiver/scripts/text/lm.py rename to perceiver/scripts/text/mlm.py index 56e8d60..0bca8b3 100644 --- a/perceiver/scripts/text/lm.py +++ b/perceiver/scripts/text/mlm.py @@ -4,7 +4,7 @@ from pytorch_lightning.cli import LightningArgumentParser, LRSchedulerTypeUnion from torch.optim import Optimizer -from perceiver.model.text.language import LitLanguageModel +from perceiver.model.text.mlm import LitMaskedLanguageModel from perceiver.scripts.cli import CLI from perceiver.scripts.utils.scheduler import CosineWithWarmupLR @@ -55,4 +55,4 @@ def configure_optimizers( if __name__ == "__main__": - MaskedLanguageModelingCLI(LitLanguageModel, description="Masked language model", run=True) + MaskedLanguageModelingCLI(LitMaskedLanguageModel, description="Masked language model", run=True) diff --git a/perceiver/scripts/text/preproc.py b/perceiver/scripts/text/preproc.py index 1b52e12..7f45708 100644 --- a/perceiver/scripts/text/preproc.py +++ b/perceiver/scripts/text/preproc.py @@ -5,6 +5,7 @@ from perceiver.data.text import ( BookCorpusDataModule, + Enwik8DataModule, ImdbDataModule, WikiBookDataModule, WikipediaDataModule, @@ -18,10 +19,20 @@ "wikibook": WikiBookDataModule, "wikitext": WikiTextDataModule, "imdb": ImdbDataModule, + "enwik8": Enwik8DataModule, } def main(args): + if args.dataset == "imdb": + from perceiver.data.text.imdb import Task + + args.task = Task[args.task] + elif args.dataset == "wikitext": + from perceiver.data.text.wikitext import Task + + args.task = Task[args.task] + DATAMODULE_CLASSES[args.dataset](**args).prepare_data() @@ -35,4 +46,5 @@ def main(args): parser.add_argument("--filter_empty", default=True, type=bool) # wikitext only parser.add_argument("--filter_headers", default=False, type=bool) # wikitext only parser.add_argument("--num_workers", default=mp.cpu_count(), type=int) + parser.add_argument("--task", default="mlm", type=str) main(parser.parse_args()) diff --git a/pyproject.toml b/pyproject.toml index 7f4bec2..cdc4a64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "perceiver-io" -version = "0.5.1" +version = "0.6.0" description = "Perceiver IO" readme = "README.md" authors = [ diff --git a/tests/language_model_conversion_test.py b/tests/language_model_conversion_test.py index eefa4db..6546306 100644 --- a/tests/language_model_conversion_test.py +++ b/tests/language_model_conversion_test.py @@ -4,8 +4,8 @@ import pytorch_lightning as pl import torch -from perceiver.model.text.language import convert_config, LanguageModel, LitLanguageModel -from perceiver.scripts.text.lm import MaskedLanguageModelingCLI +from perceiver.model.text.mlm import convert_config, LitMaskedLanguageModel, MaskedLanguageModel +from perceiver.scripts.text.mlm import MaskedLanguageModelingCLI from transformers import AutoConfig, PerceiverForMaskedLM, PerceiverTokenizer @@ -36,13 +36,13 @@ def tokenizer(): def test_conversion(source_config, source_model, tokenizer): target_config = convert_config(source_config) - target_model = LanguageModel(target_config).eval() + target_model = MaskedLanguageModel(target_config).eval() assert_equal_prediction(source_model, target_model, tokenizer) def test_conversion_lit(source_config, source_model, tokenizer): target_config = convert_config(source_config) - target_model = LitLanguageModel.create(target_config).eval() + target_model = LitMaskedLanguageModel.create(target_config).eval() assert_equal_prediction(source_model, target_model, tokenizer) @@ -57,7 +57,7 @@ def test_conversion_cli(source_model, tokenizer): "--trainer.devices=1", ], ): - cli = MaskedLanguageModelingCLI(model_class=LitLanguageModel, datamodule_class=MockDataModule, run=False) + cli = MaskedLanguageModelingCLI(model_class=LitMaskedLanguageModel, datamodule_class=MockDataModule, run=False) target_model = cli.model.eval() assert_equal_prediction(source_model, target_model, tokenizer) diff --git a/tests/masked_sample_prediction_test.py b/tests/masked_sample_filler_test.py similarity index 67% rename from tests/masked_sample_prediction_test.py rename to tests/masked_sample_filler_test.py index f3c76a7..f32eeb7 100644 --- a/tests/masked_sample_prediction_test.py +++ b/tests/masked_sample_filler_test.py @@ -3,9 +3,10 @@ import pytest import torch +import torch.nn as nn from perceiver.data.text import TextPreprocessor -from perceiver.model.text.utils import MaskedSamplePrediction +from perceiver.model.text.mlm import MaskedSampleFiller MASKED_SAMPLES = [ @@ -20,11 +21,14 @@ def preprocessor(): yield TextPreprocessor(tokenizer="bert-base-uncased", max_seq_len=64, add_special_tokens=False) -def test_fill_masks(preprocessor): - msp = MaskedSamplePredictionCallable( - targets=[["sentence", "is", "bit"], ["phrase", "was", "bunch"]], preprocessor=preprocessor +def test_fill(preprocessor): + model = MockMaskedLanguageModel( + tokenizer=preprocessor.tokenizer, targets=[["sentence", "is", "bit"], ["phrase", "was", "bunch"]] ) - masked_samples, filled_samples = msp.fill_masks(MASKED_SAMPLES, num_predictions=len(msp.targets)) + + filler = MaskedSampleFiller(preprocessor, model) + + masked_samples, filled_samples = filler.fill(MASKED_SAMPLES, num_predictions=len(model.targets)) assert masked_samples == [ "This is [MASK] one.", @@ -39,12 +43,10 @@ def test_fill_masks(preprocessor): ] -class MaskedSamplePredictionCallable(MaskedSamplePrediction): - def __init__(self, targets: List[List[str]], preprocessor: TextPreprocessor): +class MockMaskedLanguageModel(nn.Module): + def __init__(self, tokenizer, targets: List[List[str]]): super().__init__() - self.save_hyperparameters() - self.preprocessor = preprocessor - self.tokenizer = self.preprocessor.tokenizer + self.tokenizer = tokenizer self.targets = targets def forward(self, x_masked, pad_mask):

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