Comments (2)
Before it is implemented, I guess it is easier if you replace F.scaled_dot_product_attention with the given implementation.
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In order to get it supported, we will need to support the new TorchScript schema first. Also, we should probably figure out better way to reuse the conversation logic of the previously-supported ops.
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Related Issues (20)
- Do not support PReLU module? HOT 5
- torch.max not working HOT 2
- OneShotChannelPruner results in the miss of some operators HOT 4
- KeyError when executing quantization HOT 5
- PyTorch 转 TFLite 使用 int8 量化 HOT 4
- Does tinynn support following int16 quantization? HOT 1
- jit.trace succeed but tinynn tracer failed HOT 1
- It became larger after converting to tflite model HOT 4
- how to do Post-training integer quantization with int16 activation HOT 4
- unnecessary float() variables cause quantization to fail. HOT 7
- aten::index nodes take multiple indices in PyTorch model but cause an error when trying to convert to TFLite HOT 1
- aten::repeat_interleave is considered an unsupported Tensor and causing an error when trying to convert to TFLite HOT 2
- convert model error HOT 5
- 请问下 转tflite 模型能支持签名吗? HOT 9
- [PTQ Converter] 'Linear+relu' module conversion failed HOT 9
- [quantizer] activation nodes that was used multiple times will not work with OP fusion
- [converter] aten::pad with mode="circular"
- [converter] batchnorm + conv fusion
- [Converter] TFLite size is larger than expected HOT 7
- ONNX进行QAT报错
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