This is an open source implementation of CM3Leon, an autoregressive multi-modal model for text and image generation presented in the paper "Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning".
This document provides an in-depth analysis and system engineering overview of replicating the key components of the CM3Leon model presented in the paper "Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning".
CM3Leon is an autoregressive transformer-based model for text and image generation. The key ideas are:
- Retrieval augmented pretraining on a large diverse multimodal dataset
- Two-stage training: pretraining and supervised finetuning
- Contrastive decoding for improved sample quality
CM3Leon achieves state-of-the-art text-to-image generation results with 5x less compute than comparable models.
Replicating CM3Leon requires expertise in the following areas:
- Large-scale distributed training of transformer models up to 7B parameters using hundreds of GPUs/TPUs
- Efficient data loading and preprocessing to handle large multimodal datasets
- Memory optimizations to fit large models in GPU memory
- Custom tokenizers for text and image modalities
- Retrieval infrastructure for dense retrieval during pretraining
- Finetuning framework to handle mixed text-image tasks
- Inference optimizations such as compiler-accelerated decoders, lower precision, and batching
The overall system consists of:
- A distributed training framework like TensorFlow or PyTorch
- High-performance compute infrastructure (HPC cluster with GPUs/TPUs)
- A retrieval index and dense retriever module
- Data pipelines for preprocessing and loading
- Custom tokenizer code
- Model code implementing the CM3 architecture
- Finetuning framework and task datasets
- Serving infrastructure for low-latency inference
Key challenges include:
- Minimizing time to train huge models by efficiently using large compute clusters
- Avoiding bottlenecks in data loading and preprocessing
- Reducing memory usage of models during training and inference
- Optimizing inference for low latency serving
The CM3Leon architecture consists of the following components:
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Text and image tokenizers - Critical for converting text and images into discreet tokens for the transformer. Custom text tokenizer trained on CommonCrawl data. Image tokenizer from Gafni et al. 2022 that encodes 256x256 images into 1024 tokens.
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Special tokens - Uses
<break>token to indicate modality transitions. -
Retrieval augmentation - Uses bi-encoder based on CLIP to retrieve relevant text and images from memory bank. Retrieval promotes diversity through query dropout and filtering highly similar documents.
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Autoregressive decoder-only transformer - Standard transformer architecture initialized similarly to GPT models. Trained on CM3 infilling objective.
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Two-stage training
- Pretraining with retrieval augmentation
- Supervised finetuning on text-image tasks via instruction tuning
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Contrastive decoding - Modified contrastive decoding blends conditional and unconditional samples for better quality.
The model dimensions range from 350M to 7B parameters.
Key implementation requirements are a distributed training framework that can scale to handle models with billions of parameters using model parallelism, and integration with custom tokenizers. Memory optimization techniques like mixed precision and activation recomputation may be needed to fit large models.
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For pretraining, requires a large (100M+ examples) diverse multimodal dataset like Shutterstock. Each example consists of an image + caption pair.
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For finetuning, a mixture of text and image tasks with accompanying datasets. Tasks include text-to-image generation, image captioning, visual QA, etc.
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Data loading should be fast and scalable, not a bottleneck to model training. Using efficient formats like TFRecord, parallel decoders, caching, etc can help.
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Some image preprocessing is required, like resizing images to 256x256 pixels. Text data needs tokenization.
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Special care needs to be taken to avoid shuffling text and image pairs during preprocessing.
A data engineering team is needed to build and maintain data pipelines, integrate new datasets, and ensure efficient data delivery.
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CM3Leon uses two training stages:
- Pretraining with retrieval augmentation and CM3 objective
- Supervised finetuning on text-image tasks
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Pretraining is compute intensive, requiring hundreds of GPUs/TPUs and datasets of at least 100M examples.
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Training infrastructure needs to scale to handle model parallelism over many accelerators. Frameworks like TensorFlow Mesh and PyTorch distributed training are useful.
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CNN backbone needs to be frozen after pretraining CNN weights end-to-end.
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Retrieval index needs to be built and integrated with training flow for augmentation.
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Heavy hyperparameter tuning is required for learning rates, batch sizes, optimizers, etc.
Training such large models requires a team of deep learning researchers, data scientists, and HPC engineers to optimize and orchestrate distributed training jobs on a cluster. Careful tracking of metrics is needed.
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Autoregressive sampling is inherently sequential making inference latency a challenge.
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Compiler accelerated decoders like FasterTransformer can greatly improve throughput. Other optimizations like lower precision (FP16/INT8) and batching also help.
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Contrastive decoding further increases compute requirements over vanilla sampling. Needs to be implemented efficiently.
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Retrieval index needs to be serving ready for low-latency augmentation during inference.
Latency and cost requirements for serving systems need to be accounted for. A serving infrastructure team is recommended for optimal deployment.
Replicating all capabilities of CM3Leon is a substantial engineering effort, requiring expertise in large-scale deep learning, data engineering, and infrastructure optimization. The key factors are assembling a large and diverse training dataset, efficient distributed training framework, mixture of pretraining and finetuning objectives, and optimizations for low-latency inference. With sufficient resources and a capable team, the results of the paper can be replicated.
This analysis covers the major technical components and challenges involved in reimplementing CM3Leon. Please let me know if I got anything wrong.
The paper does not explicitly mention which retrieval store or index is used for the dense retriever in CM3Leon.
However, based on the description in the paper, it likely uses a standard dense vector index and maximum inner product search (MIPS) for efficient retrieval, similar to tools like FAISS or Annoy.
Specifically, the paper states:
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They use a bi-encoder dense retriever based on CLIP to encode text and images into dense vectors.
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The text and image embeddings are indexed in a memory bank.
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At training time, relevance scores are computed between the query and documents in the bank using maximum inner product search.
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The topmost relevant documents are retrieved for augmentation.
This setup is very common in dense retrieval systems where the index serves as a high-performance nearest neighbor search for vectors.
Some potential options for the retrieval store include:
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FAISS - Open source library from Facebook for efficient similarity search over float vectors. Supports various index structures and distance measures.
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Annoy - Approximate nearest neighbor search library with support for angular distance metrics. Can be used to index CLIP embeddings.
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Milvus - Open source vector database optimized for MIPS queries over huge datasets.
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Pinecone - Managed vector database with API for text and image search
Since the model was likely trained at Facebook, it's reasonable to assume they used an internally developed index or a highly optimized version of FAISS. But any dense vector index with MIPS support can replicate the retrieval functionality.
The paper does not go into these engineering details but a standard dense vector index is sufficient to implement the retrieval augmented training process.