Attention-based deep learning models such as the transformers are highly effective in capturing relationship between tokens in an input sequence, even across long distances. As a result, they are used with text, image, and sound-based inputs, where the sequence length can be in thousands of tokens. However, despite the effectiveness of attention modules to capture long term dependencies, in practice, their application to long sequence input is limited by compute and memory requirements of the attention computation that grow quadratically, `O(n^2)`, with the sequence length `n`.
Attention-based deep learning models such as the transformers are highly effective in capturing relationship between tokens in an input sequence, even across long distances. As a result, they are used with text, image, and sound-based inputs, where the sequence length can be in thousands of tokens. However, despite the effectiveness of attention modules to capture long term dependencies, in practice, their application to long sequence input is limited by compute and memory requirements of the attention computation that grow quadratically, `O(n^2)`, with the sequence length `n`.
Today we are announcing the release of ZeRO-3 Offload, a highly efficient and easy to use implementation of ZeRO Stage 3 and ZeRO Offload combined, geared towards our continued goal of democratizing AI by making efficient large-scale DL training available to everyone. The key benefits of ZeRO-3 Offload are:
Today we are announcing the release of ZeRO-3 Offload, a highly efficient and easy to use implementation of ZeRO Stage 3 and ZeRO Offload combined, geared towards our continued goal of democratizing AI by making efficient large-scale DL training available to everyone. The key benefits of ZeRO-3 Offload are:
While DeepSpeed supports training advanced large-scale models, using these trained models in the desired application scenarios is still challenging due to three major limitations in existing inference solutions: 1) lack of support for multi-GPU inference to fit large models and meet latency requirements, 2) limited GPU kernel performance when running inference with small batch sizes, and 3) difficulties in exploiting quantization, which includes both quantizing the model to reduce the model size and latency as well as supporting high-performance inference of quantized models without specialized hardware.
While DeepSpeed supports training advanced large-scale models, using these trained models in the desired application scenarios is still challenging due to three major limitations in existing inference solutions: 1) lack of support for multi-GPU inference to fit large models and meet latency requirements, 2) limited GPU kernel performance when running inference with small batch sizes, and 3) difficulties in exploiting quantization, which includes both quantizing the model to reduce the model size and latency as well as supporting high-performance inference of quantized models without specialized hardware.
The field of Artificial Intelligence-Generated Content (AIGC) is rapidly growing, with the goal of making content creation more efficient and accessible. One of the most exciting areas of AIGC is the development of large-scale multi-modal models like [Flamingo](https://arxiv.org/abs/2204.14198), [BLIP](https://arxiv.org/abs/2301.12597), and [GPT4](https://arxiv.org/abs/2303.08774), which can accept inputs from multiple resources, e.g., image, text, audio, etc., and generate a variety of formats as outputs. For example, image creation can be made through stable diffusion and DALLE using the prompt text, and the new feature in the coming Office can create slides with texts, images, animations, etc., by leveraging the power of the new Microsoft Office Copilot.
The field of Artificial Intelligence-Generated Content (AIGC) is rapidly growing, with the goal of making content creation more efficient and accessible. One of the most exciting areas of AIGC is the development of large-scale multi-modal models like [Flamingo](https://arxiv.org/abs/2204.14198), [BLIP](https://arxiv.org/abs/2301.12597), and [GPT4](https://arxiv.org/abs/2303.08774), which can accept inputs from multiple resources, e.g., image, text, audio, etc., and generate a variety of formats as outputs. For example, image creation can be made through stable diffusion and DALLE using the prompt text, and the new feature in the coming Office can create slides with texts, images, animations, etc., by leveraging the power of the new Microsoft Office Copilot.
