infery-examples
fastT5
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| infery-examples | fastT5 | |
|---|---|---|
| 1 | 5 | |
| 50 | 540 | |
| - | - | |
| 0.0 | 0.0 | |
| 5 months ago | about 1 year ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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infery-examples
fastT5
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Speeding up T5
I've tried https://github.com/Ki6an/fastT5 but it works with CPU only.
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Convert Pegasus model to ONNX
I am working on a project where I fine-tuned a Pegasus model on the Reddit dataset. Now, I need to convert the fine-tuned model to ONNX for the deployment stage. I have followed this guide from Huggingface to convert to the ONNX model for unsupported architects. I got it done but the ONNX model can't generate text. Turned out that Pegasus is an encoder-decoder model and most guides are for either encoder-model (e.g. BERT) or decoder-model (e.g. GPT2). I found the only example of converting an encoder-decoder model to ONNX from here https://github.com/Ki6an/fastT5.
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[P] What we learned by making T5-large 2X faster than Pytorch (and any autoregressive transformer)
Microsoft Onnx Runtime T5 export tool / FastT5: to support caching, it exports 2 times the decoder part, one with cache, and one without (for the first generated token). So the memory footprint is doubled, which makes the solution difficult to use for these large transformer models.
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Conceptually, what are the "Past key values" in the T5 Decoder?
Here is the fastT5 model code for reference code:https://github.com/Ki6an/fastT5/blob/master/fastT5/onnx_models.py
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[P] boost T5 models speed up to 5x & reduce the model size by 3x using fastT5.
for more information on the project refer to the repository here.
What are some alternatives?
tensorflow-onnx - Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
Questgen.ai - Question generation using state-of-the-art Natural Language Processing algorithms
hand-gesture-recognition-mediapipe - This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points. Handpose is estimated using MediaPipe.
mt5-M2M-comparison - Comparing M2M and mT5 on a rare language pairs, blog post: https://medium.com/@abdessalemboukil/comparing-facebooks-m2m-to-mt5-in-low-resources-translation-english-yoruba-ef56624d2b75
x-stable-diffusion - Real-time inference for Stable Diffusion - 0.88s latency. Covers AITemplate, nvFuser, TensorRT, FlashAttention. Join our Discord communty: https://discord.com/invite/TgHXuSJEk6
json-translate - Translate json files with DeepL or AWS
nebuly - The user analytics platform for LLMs
frame-semantic-transformer - Frame Semantic Parser based on T5 and FrameNet
timm-flutter-pytorch-lite-blogpost - PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter.
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
EdgeSAM - Official PyTorch implementation of "EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM"
FasterTransformer - Transformer related optimization, including BERT, GPT