Comments (3)
unfortunately no. quantized onnx models can only be run on CPU & onnxruntime-gpu
does not support quantization.
if you want more details on this question you should create an issue in onnxruntime.
from fastt5.
@Ki6an HI, do we support onnxruntime-gpu mode with V100 or T4 if dont use the quantization?
i see with onnxruntime only we can have x2 speedup ?
from fastt5.
@mblank5 no, this library uses onnxruntime, and to support GPUs you need to have onnxruntime-GPU
installed.
BUT you can uninstall onnxruntime
after the fastt5 library is installed, and install onnxruntime-gpu
and try running the model but not sure you'll get speed up. for more info refer to this issue.
with onnxruntime
, you'll get speed up if you are using modern CPUs and more CPU cores. refer benchmark section of README.
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Related Issues (20)
- Support for py3.10 HOT 1
- Fails to convert T0-3B HOT 2
- Mt5 model loading fails HOT 11
- Is it suit for other translation model like "Helsinki-NLP / opus-mt-en-de"? HOT 1
- Decoder's encoder_attention_mask input should be called decoder_attention_mask HOT 1
- Thank You and Demo Running in the Browser HOT 2
- M2M100 to ONNX
- Upgrade ONNX runtime HOT 1
- fastt5 not working with FastAPI gunicorn and docker HOT 1
- No such file or directory: '/content/encoder.embed_tokens.weight' HOT 3
- In the source code, the use of attention_mask is contradictory HOT 1
- Issue with onnxruntime HOT 4
- flan-t5 support HOT 5
- GPU support
- Segmentation fault (core dumped) HOT 5
- Is fastT5 qunatization slower than pytorch dynamic quantization?
- TypeError: quantize_dynamic() got an unexpected keyword argument 'activation_type' HOT 1
- Dead kernel
- TypeError: quantize_dynamic() got an unexpected keyword argument 'activation_type' HOT 4
- Does UMT5 Supports? HOT 2
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