Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?
Paper • 2604.03619 • Published • 8
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Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?
LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
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11091
# PyTorch == 2.5.0 Breaks Transformers' SDPAttention!
When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."
We can use workaround like this:
but this slow downs the performance gain from PyTorch 2.5.
Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)
Fastest way for now : pip install "torch<2.5"
Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273
When you encounter "RuntimeError: cuDNN Frontend error: [cudnn_frontend] Error: No execution plans support the graph."
We can use workaround like this:
torch.backends.cuda.enable_cudnn_sdp(False)but this slow downs the performance gain from PyTorch 2.5.
Although it is fixed(not "fixed" but default option is turn-off the cuDNN SDPA) at here -- https://github.com/pytorch/pytorch/pull/138587 , but not released yet. (you need to install directly from source)
Fastest way for now : pip install "torch<2.5"
Ref: https://github.com/huggingface/diffusers/issues/9704#issuecomment-2422585273
Post
19088
#TPU #PyTorch #Jax
When You're trying to use PyTorch or Jax on TPU,
for v2/v3/v4:
use tpu-ubuntu2204-base
for v5p:
use v2-alpha-tpuv5
for v5e:
use v2-alpha-tpuv5-lite
You must use these base images for the system to 'boot'.
Previously used tpu-vm-v4-pt-1.13 images might seem to start the VM, but SSH connections do not work.
I thought it was a firewall issue and spent a lot of time on it before realizing it was a problem with the boot image 🥲
https://cloud.google.com/tpu/docs/runtimes#pytorch_and_jax
When You're trying to use PyTorch or Jax on TPU,
for v2/v3/v4:
use tpu-ubuntu2204-base
for v5p:
use v2-alpha-tpuv5
for v5e:
use v2-alpha-tpuv5-lite
You must use these base images for the system to 'boot'.
Previously used tpu-vm-v4-pt-1.13 images might seem to start the VM, but SSH connections do not work.
I thought it was a firewall issue and spent a lot of time on it before realizing it was a problem with the boot image 🥲
https://cloud.google.com/tpu/docs/runtimes#pytorch_and_jax
Post
12340
🚀 **InfiniTransformer, Gemma/Llama3 based Implementation!** 🌌
> Update @ 2024.04.19: It now supports Llama-3!
> Note: this implementation is unofficial
This implementation is designed to handle virtually infinite context lengths.
Here's the github repo: https://github.com/Beomi/InfiniTransformer
📄 **Read the original Paper:** https://arxiv.org/abs/2404.07143
## **Focus on Infini-Attention**
- **2 Types of Implementation available:** Attention-layer only implementation / Model & Train-wise implementation
- **Fixed(segment dependent) Memory Usage:** Enables training on larger models and longer sequences without the memory overhead typical of standard Transformer implementations.
- **Infinite Context Capability:** Train with unprecedented sequence lengths—imagine handling up to 1 million sequence lengths on standard hardware!
- You could train Gemma-2B with 1M sequence length with 2K segmentation size with single H100 GPU.
## **Try InfiniTransformer**
1. **Clone the repository:**
2. **Install necessary tools:**
3. **Dive Deep into Custom Training:**
- Train with extensive sequence lengths using scripts such as
for more detailed info, please visit Repo: https://github.com/Beomi/InfiniTransformer
Look forward to see your feedbacks! 😊
ps. Training loss plot is here 😉
> Update @ 2024.04.19: It now supports Llama-3!
> Note: this implementation is unofficial
This implementation is designed to handle virtually infinite context lengths.
Here's the github repo: https://github.com/Beomi/InfiniTransformer
📄 **Read the original Paper:** https://arxiv.org/abs/2404.07143
## **Focus on Infini-Attention**
- **2 Types of Implementation available:** Attention-layer only implementation / Model & Train-wise implementation
- **Fixed(segment dependent) Memory Usage:** Enables training on larger models and longer sequences without the memory overhead typical of standard Transformer implementations.
- **Infinite Context Capability:** Train with unprecedented sequence lengths—imagine handling up to 1 million sequence lengths on standard hardware!
- You could train Gemma-2B with 1M sequence length with 2K segmentation size with single H100 GPU.
## **Try InfiniTransformer**
1. **Clone the repository:**
bash
git clone https://github.com/Beomi/InfiniTransformer
2. **Install necessary tools:**
bash
pip install -r requirements.txt
pip install -e git+https://github.com/huggingface/transformers.git@b109257f4f#egg=transformers
3. **Dive Deep into Custom Training:**
- Train with extensive sequence lengths using scripts such as
./train.gemma.infini.noclm.1Mseq.sh.for more detailed info, please visit Repo: https://github.com/Beomi/InfiniTransformer
Look forward to see your feedbacks! 😊
ps. Training loss plot is here 😉
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